Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning
Open accessAbstract
Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.
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Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles and enabling iterative design.
“Identifying key descriptors or features in a structure-property relationship mapping has been demonstrated as an approach to revealing design principles (18,22,32,52) and enabling iterative design (70).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [18]
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1 retracted
RETRACTED ARTICLE: A universal principle for a rational design of single-atom electrocatalysts
Xu, H., Cheng, D., Cao, D., & Zeng, X. C. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0063-z · Cited by 1,586 · see citing works
Retracted(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Zhao, Y., & Truhlar, D. G. (2007) · Theoretical Chemistry Accounts · doi:10.1007/s00214-007-0310-x · Cited by 30,407 · see citing works
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
Electron-energy-loss spectra and the structural stability of nickel oxide: An LSDA+U study
Dudarev, S. L., Botton, G. A., Savrasov, S. Y., Humphreys, C. J., & Sutton, A. P. (1998) · Physical review. B, Condensed matter · doi:10.1103/physrevb.57.1505 · Cited by 15,260 · see citing works
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094 · see citing works
Dewar, M. J., Zoebisch, E., Healy, E. F., & Stewart, J. (1985) · Journal of the American Chemical Society · doi:10.1021/ja00299a024 · Cited by 11,560 · see citing works
Open Babel: An open chemical toolbox
O'Boyle, N. M., Banck, M. S., James, C. A. J., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011) · Journal of Cheminformatics · doi:10.1186/1758-2946-3-33 · Cited by 10,705 · see citing works
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,603 · see citing works
The Cambridge Structural Database
Groom, C. R., Bruno, I. J., Lightfoot, M. P., & Ward, S. C. (2016) · Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials · doi:10.1107/s2052520616003954 · Cited by 10,297 · see citing works
A survey of Hammett substituent constants and resonance and field parameters
Hansch, C. H., Leo, A. J., & TAFT, R. W. (1991) · Chemical Reviews · doi:10.1021/cr00002a004 · Cited by 8,676 · see citing works
Band theory and Mott insulators: Hubbard U instead of Stoner I
Anisimov, V. I., Zaanen, J., & Andersen, O. K. (1991) · Physical review. B, Condensed matter · doi:10.1103/physrevb.44.943 · Cited by 7,492 · see citing works
Weininger, D. (1988) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00057a005 · Cited by 7,436 · see citing works
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101 · see citing works
DREIDING: a generic force field for molecular simulations
Mayo, S. L., Olafson, B. D., & Goddard, W. A. (1990) · The Journal of Physical Chemistry · doi:10.1021/j100389a010 · Cited by 6,592 · see citing works
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y., & Ghahramani, Z. (2015) · arXiv · doi:10.48550/arxiv.1506.02142 · Cited by 6,471 · see citing works
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45356-3_83 · Cited by 5,534 · see citing works
Zhao, Y., & Truhlar, D. G. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2370993 · Cited by 5,161 · see citing works
Steric effects of phosphorus ligands in organometallic chemistry and homogeneous catalysis
Tolman, C. A. (1977) · Chemical Reviews · doi:10.1021/cr60307a002 · Cited by 4,824 · see citing works
Density-functional theory and strong interactions: Orbital ordering in Mott-Hubbard insulators
Liechtenstein, A. I., Anisimov, V. I., & Zaanen, J. (1995) · Physical review. B, Condensed matter · doi:10.1103/physrevb.52.r5467 · Cited by 4,819 · see citing works
Single-Atom Catalysts: A New Frontier in Heterogeneous Catalysis
Yang, X.-F., Wang, A., Qiao, B., Li, J., Liu, J., & Zhang, T. (2013) · Accounts of Chemical Research · doi:10.1021/ar300361m · Cited by 4,783 · see citing works
Universality in Oxygen Evolution Electrocatalysis on Oxide Surfaces
Man, I. C., Su, H., Calle‐Vallejo, F., Hansen, H. A., Martínez, J. I., Inoglu, N. G., Kitchin, J., Jaramillo, T. F., Nørskov, J. K., & Rossmeisl, J. (2011) · ChemCatChem · doi:10.1002/cctc.201000397 · Cited by 4,769 · see citing works
The atomic simulation environment—a Python library for working with atoms
Larsen, A. H., Mortensen, J. J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., et al. (2017) · Journal of Physics Condensed Matter · doi:10.1088/1361-648x/aa680e · Cited by 4,551 · see citing works
Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
Ong, S. P., Richards, W. D., Jain, A., Hautier, G., Kocher, M., Cholia, S., Gunter, D., Chevrier, V. L., Persson, K. A., & Ceder, G. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.10.028 · Cited by 4,339 · see citing works
Cordero, B., Gómez, V., Platero‐Prats, A. E., Revés, M., Echeverría, J., Cremades, E., Barragán, F., & Álvarez, S. (2008) · Dalton Transactions · doi:10.1039/b801115j · Cited by 4,185 · see citing works
Towards the computational design of solid catalysts
Nørskov, J. K., Bligaard, T., Rossmeisl, J., & Christensen, C. H. (2009) · Nature Chemistry · doi:10.1038/nchem.121 · Cited by 4,161 · see citing works
Elstner, M., Porezag, D. V., Jungnickel, G., Elsner, J., Haugk, M., Frauenheim, T., Suhai, S., & Seifert, G. (1998) · Physical review. B, Condensed matter · doi:10.1103/physrevb.58.7260 · Cited by 4,130 · see citing works
Cramer, R. D., Patterson, D. E., & Bunce, J. D. (1988) · Journal of the American Chemical Society · doi:10.1021/ja00226a005 · Cited by 3,931 · see citing works
Cococcioni, M., & de Gironcoli, S. (2005) · Physical Review B · doi:10.1103/physrevb.71.035105 · Cited by 3,699 · see citing works
Stewart, J. J. P. (2007) · Journal of Molecular Modeling · doi:10.1007/s00894-007-0233-4 · Cited by 3,652 · see citing works
The Development of L 2 X 2 RuCHR Olefin Metathesis Catalysts: An Organometallic Success Story
Trnka, T. M., & Grubbs, R. H. (2000) · Accounts of Chemical Research · doi:10.1021/ar000114f · Cited by 3,561 · see citing works
Eichkorn, K., Weigend, F., Treutler, O., & Ahlrichs, R. (1997) · Theoretical Chemistry Accounts · doi:10.1007/s002140050244 · Cited by 3,471 · see citing works
New software for searching the Cambridge Structural Database and visualizing crystal structures
Bruno, I. J., Cole, J. C., Edgington, P. R., Kessler, M., Macrae, C. F., McCabe, P., Pearson, J., & Taylor, R. (2002) · Acta Crystallographica Section B Structural Science · doi:10.1107/s0108768102003324 · Cited by 3,134 · see citing works
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106 · see citing works
Density-Functional Theory for Fractional Particle Number: Derivative Discontinuities of the Energy
Perdew, J. P., Parr, R. G., Levy, M., & Balduz, J. L. (1982) · Physical Review Letters · doi:10.1103/physrevlett.49.1691 · Cited by 3,004 · see citing works
Configuration interaction calculations on the nitrogen molecule
Langhoff, S. R., & Davidson, E. R. (1974) · International Journal of Quantum Chemistry · doi:10.1002/qua.560080106 · Cited by 2,910 · see citing works
Auxiliary basis sets to approximate Coulomb potentials
Eichkorn, K., Treutler, O., Öhm, H., Häser, M., & Ahlrichs, R. (1995) · Chemical Physics Letters · doi:10.1016/0009-2614(95)00621-a · Cited by 2,869 · see citing works
Xie, T., & Grossman, J. C. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.145301 · Cited by 2,828 · see citing works
Molecular mechanics. The MM3 force field for hydrocarbons. 1
Allinger, N. L., Yuh, Y. H., & Lii, J. (1989) · Journal of the American Chemical Society · doi:10.1021/ja00205a001 · Cited by 2,804 · see citing works
Quantum Tunneling of Magnetization and Related Phenomena in Molecular Materials
Gatteschi, D., & Sessoli, R. (2003) · Angewandte Chemie International Edition · doi:10.1002/anie.200390099 · Cited by 2,802 · see citing works
Oxidation energies of transition metal oxides within the GGA + U framework
Wang, L., Maxisch, T., & Ceder, G. (2006) · Physical Review B · doi:10.1103/physrevb.73.195107 · Cited by 2,751 · see citing works
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666 · see citing works
Hagfeldt, A., & Grätzel, M. (2000) · Accounts of Chemical Research · doi:10.1021/ar980112j · Cited by 2,613 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
Physical Content of the Exact Kohn-Sham Orbital Energies: Band Gaps and Derivative Discontinuities
Perdew, J. P., & Levy, M. (1983) · Physical Review Letters · doi:10.1103/physrevlett.51.1884 · Cited by 2,563 · see citing works
Insights into Current Limitations of Density Functional Theory
Cohen, A. J., Mori-Sánchez, P., & Yang, W. (2008) · Science · doi:10.1126/science.1158722 · Cited by 2,527 · see citing works
Saal, J. E., Kirklin, S. J., Aykol, M., Meredig, B., & Wolverton, C. M. (2013) · JOM · doi:10.1007/s11837-013-0755-4 · Cited by 2,514 · see citing works
Automated exploration of the low-energy chemical space with fast quantum chemical methods
Pracht, P., Bohle, F., & Grimme, S. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/c9cp06869d · Cited by 2,397 · see citing works
Recent advances in surrogate-based optimization
Forrester, A. I. J., & Keane, A. J. (2009) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2008.11.001 · Cited by 2,382 · see citing works
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
The Effect of Structure upon the Reactions of Organic Compounds. Benzene Derivatives
Hammett, L. P. (1937) · Journal of the American Chemical Society · doi:10.1021/ja01280a022 · Cited by 2,256 · see citing works
The merger of transition metal and photocatalysis
Twilton, J., Le, C. “., Musacchio, P. Z., Shaw, M. H., Evans, R. W., & MacMillan, D. W. C. (2017) · Nature Reviews Chemistry · doi:10.1038/s41570-017-0052 · Cited by 2,231 · see citing works
Simmons, E. M., & Hartwig, J. F. (2012) · Angewandte Chemie International Edition · doi:10.1002/anie.201107334 · Cited by 2,146 · see citing works
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053 · see citing works
Grimme, S., Bannwarth, C., & Shushkov, P. G. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00118 · Cited by 2,050 · see citing works
Stewart, J. J. P. (2012) · Journal of Molecular Modeling · doi:10.1007/s00894-012-1667-x · Cited by 2,028 · see citing works
Thermally Activated Delayed Fluorescence Materials Towards the Breakthrough of Organoelectronics
Tao, Y., Yuan, K., Chen, T., Xu, P., Li, H., Chen, R., Zheng, C., Zhang, L., & Huang, W. (2014) · Advanced Materials · doi:10.1002/adma.201402532 · Cited by 2,002 · see citing works
An efficient and near linear scaling pair natural orbital based local coupled cluster method
Riplinger, C., & Neese, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4773581 · Cited by 1,969 · see citing works
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966 · see citing works
Density-Functional Theory of the Energy Gap
Sham, L. J., & Schlüter, M. (1983) · Physical Review Letters · doi:10.1103/physrevlett.51.1888 · Cited by 1,938 · see citing works
Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
Willems, T., Rycroft, C. H., Kazi, M., Meza, J. C., & Harańczyk, M. (2011) · Microporous and Mesoporous Materials · doi:10.1016/j.micromeso.2011.08.020 · Cited by 1,935 · see citing works
Natural triple excitations in local coupled cluster calculations with pair natural orbitals
Riplinger, C., Sandhoefer, B., Hansen, A., & Neese, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4821834 · Cited by 1,893 · see citing works
How to Conceptualize Catalytic Cycles? The Energetic Span Model
Kozuch, S., & Shaik, S. S. (2010) · Accounts of Chemical Research · doi:10.1021/ar1000956 · Cited by 1,868 · see citing works
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847 · see citing works
A general-purpose machine learning framework for predicting properties of inorganic materials
Ward, L., Agrawal, A., Choudhary, A., & Wolverton, C. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2016.28 · Cited by 1,831 · see citing works
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.
Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813 · see citing works
Perspective on density functional theory
Burke, K. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4704546 · Cited by 1,758 · see citing works
Farha, O. K., Yazaydın, A. Ö., Eryazici, I., Malliakas, C. D., Hauser, B. G., Kanatzidis, M. G., Nguyen, S. T., Snurr, R. Q., & Hupp, J. T. (2010) · Nature Chemistry · doi:10.1038/nchem.834 · Cited by 1,746 · see citing works
Machine-learning-assisted materials discovery using failed experiments
Raccuglia, P., Elbert, K. C., Adler, P. D. F., Falk, C., Wenny, M. B., Mollo, A., Zeller, M., Friedler, S. A., Schrier, J., & Norquist, A. J. (2016) · Nature · doi:10.1038/nature17439 · Cited by 1,727 · see citing works
Extended tight‐binding quantum chemistry methods
Bannwarth, C., Caldeweyher, E., Ehlert, S., Hansen, A., Pracht, P., Seibert, J., Spicher, S., & Grimme, S. (2020) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1493 · Cited by 1,711 · see citing works
Thermochemistry of Proton-Coupled Electron Transfer Reagents and its Implications
Warren, J. J., Tronic, T. A., & Mayer, J. M. (2010) · Chemical Reviews · doi:10.1021/cr100085k · Cited by 1,703 · see citing works
Density functional theory for transition metals and transition metal chemistry
Cramer, C. J., & Truhlar, D. G. (2009) · Physical Chemistry Chemical Physics · doi:10.1039/b907148b · Cited by 1,647 · see citing works
Guest-Dependent Spin Crossover in a Nanoporous Molecular Framework Material
Halder, G. J., Kepert, C. J., Moubaraki, B., Murray, K. S., & Cashion, J. D. (2002) · Science · doi:10.1126/science.1075948 · Cited by 1,568 · see citing works
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552 · see citing works
Perspective: Fifty years of density-functional theory in chemical physics
Becke, A. D. (2014) · The Journal of Chemical Physics · doi:10.1063/1.4869598 · Cited by 1,542 · see citing works
Pavlishchuk, V. V., & Addison, A. W. (2000) · Inorganica Chimica Acta · doi:10.1016/s0020-1693(99)00407-7 · Cited by 1,531 · see citing works
AFLOW: An automatic framework for high-throughput materials discovery
Stefano Curtarolo, A., Setyawan, W., Hart, G. L. W., Jahnátek, M., Chepulskii, R. V., Taylor, R. H., Wang, S., Junkai Xue, A., Yang, K., Levy, O., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.005 · Cited by 1,521 · see citing works
Applications of functionalized transition metal complexes in photonic and optoelectronic devices
Kalyanasundaram, K. (1998) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(98)00189-1 · Cited by 1,516 · see citing works
Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505 · see citing works
The Spectra and Electronic Structure of the Tetrahedral Ions MnO4−, CrO4−−, and ClO4−
Wolfsberg, M., & Helmholz, L. (1952) · The Journal of Chemical Physics · doi:10.1063/1.1700580 · Cited by 1,490 · see citing works
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,487 · see citing works
Wellendorff, J., Lundgaard, K. T., Møgelhøj, A., Petzold, V., Landis, D. D., Nørskov, J. K., Bligaard, T., & Jacobsen, K. W. (2012) · Physical Review B · doi:10.1103/physrevb.85.235149 · Cited by 1,450 · see citing works
Large-scale screening of hypothetical metal–organic frameworks
Wilmer, C. E., Leaf, M., Lee, C. Y., Farha, O. K., Hauser, B. G., Hupp, J. T., & Snurr, R. Q. (2012) · Nature Chemistry · doi:10.1038/nchem.1192 · Cited by 1,439 · see citing works
Universality in Heterogeneous Catalysis
Nørskov, J. K., Bligaard, T., Logadóttir, Á., Bahn, S. R., Hansen, L. B., Bollinger, M. V., Bengaard, H. S., Hammer, B., Šljivančanin, Ž., Mavrikakis, M., et al. (2002) · Journal of Catalysis · doi:10.1006/jcat.2002.3615 · Cited by 1,408 · see citing works
A diagnostic for determining the quality of single-reference electron correlation methods
Lee, T. J., & Taylor, P. R. (2009) · International Journal of Quantum Chemistry · doi:10.1002/qua.560360824 · Cited by 1,395 · see citing works
Agrawal, A., & Choudhary, A. (2016) · APL Materials · doi:10.1063/1.4946894 · Cited by 1,385 · see citing works
Molecular spin crossover phenomenon: recent achievements and prospects
Bousseksou, A., Molnár, G., Salmon, L., & Nicolazzi, W. (2011) · Chemical Society Reviews · doi:10.1039/c1cs15042a · Cited by 1,379 · see citing works
An object-oriented scripting interface to a legacy electronic structure code
Bahn, S. R., & Jacobsen, K. W. (2002) · Computing in Science & Engineering · doi:10.1109/5992.998641 · Cited by 1,336 · see citing works
Reiher, M., Salomon, O., & Heß, B. A. (2001) · Theoretical Chemistry Accounts · doi:10.1007/s00214-001-0300-3 · Cited by 1,324 · see citing works
Mori‐Sánchez, P., Cohen, A. J., & Yang, W. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.146401 · Cited by 1,303 · see citing works
Towards the online computer-aided design of catalytic pockets
Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290 · see citing works
Widegren, J. A., & Finke, R. G. (2003) · Journal of Molecular Catalysis A Chemical · doi:10.1016/s1381-1169(02)00728-8 · Cited by 1,246 · see citing works
Zerner, M. C., Loew, G. H., Kirchner, R. F., & Mueller‐Westerhoff, U. T. (1980) · Journal of the American Chemical Society · doi:10.1021/ja00522a025 · Cited by 1,227 · see citing works
Shape maps and polyhedral interconversion paths in transition metal chemistry
Álvarez, S., Alemany, P., Casanova, D., Cirera, J., Llunell, M., & Avnir, D. (2005) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2005.03.031 · Cited by 1,135 · see citing works
Stefano Curtarolo, A., Setyawan, W., Wang, S., Junkai Xue, A., Yang, K., Taylor, R. H., Nelson, L. J., Hart, G. L. W., Sanvito, S., Nardelli, M. B., et al. (2012) · Computational Materials Science · doi:10.1016/j.commatsci.2012.02.002 · Cited by 1,127 · see citing works
Unsupervised word embeddings capture latent knowledge from materials science literature
Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G., & Jain, A. (2019) · Nature · doi:10.1038/s41586-019-1335-8 · Cited by 1,123 · see citing works
DFTB3: Extension of the Self-Consistent-Charge Density-Functional Tight-Binding Method (SCC-DFTB)
Gaus, M., Cui, Q., & Elstner, M. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct100684s · Cited by 1,111 · see citing works
Decurtins, S., Gütlich, P., Köhler, C., Spiering, H., & Hauser, A. (1984) · Chemical Physics Letters · doi:10.1016/0009-2614(84)80403-0 · Cited by 1,107 · see citing works
Matminer: An open source toolkit for materials data mining
Ward, L. T., Dunn, A., Faghaninia, A., Zimmermann, N., Bajaj, S., Wang, Q. S., Montoya, J. H., Chen, J., Bystrom, K., Dylla, M. T., et al. (2018) · Computational Materials Science · doi:10.1016/j.commatsci.2018.05.018 · Cited by 1,091 · see citing works
Ferrocene as an internal standard for electrochemical measurements
Gagnè, R. R., Koval, C. A., & Lisensky, G. C. (1980) · Inorganic Chemistry · doi:10.1021/ic50211a080 · Cited by 1,083 · see citing works
A robotic platform for flow synthesis of organic compounds informed by AI planning
Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V. L., Hart, T., Fishman, J., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,063 · see citing works
Bayesian reaction optimization as a tool for chemical synthesis
Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., & Doyle, A. G. (2021) · Nature · doi:10.1038/s41586-021-03213-y · Cited by 1,063 · see citing works
Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., et al. (2016) · Nature Materials · doi:10.1038/nmat4717 · Cited by 1,060 · see citing works
Predicting reaction performance in C–N cross-coupling using machine learning
Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058 · see citing works
Ghigo, G., Roos, B. O., & Malmqvist, P. (2004) · Chemical Physics Letters · doi:10.1016/j.cplett.2004.08.032 · Cited by 1,045 · see citing works
Nanoscience and Technology: A Collection of Reviews from Nature Journals
Rinaldi, G. (2010) · Assembly Automation · doi:10.1108/aa.2010.03330bae.001 · Cited by 1,030 · see citing works
Mardirossian, N., & Head-Gordon, M. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp54374a · Cited by 986 · see citing works
FABIAN, J., Nakazumi, H., & Matsuoka, M. (1992) · Chemical Reviews · doi:10.1021/cr00014a003 · Cited by 968 · see citing works
Structure:function relationships in molecular spin-crossover complexes
Halcrow, M. A. (2011) · Chemical Society Reviews · doi:10.1039/c1cs15046d · Cited by 956 · see citing works
Excitation Gaps of Finite-Sized Systems from Optimally Tuned Range-Separated Hybrid Functionals
Kronik, L., Stein, T., Refaely‐Abramson, S., & Baer, R. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct2009363 · Cited by 943 · see citing works
Ufimtsev, I. S., & Martı́nez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9003004 · Cited by 924 · see citing works
SambVca 2. A Web Tool for Analyzing Catalytic Pockets with Topographic Steric Maps
Falivene, L., Credendino, R., Poater, A., Petta, A., Serra, L., Oliva, R., Scarano, V., & Cavallo, L. (2016) · Organometallics · doi:10.1021/acs.organomet.6b00371 · Cited by 912 · see citing works
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911 · see citing works
Data-driven design of metal–organic frameworks for wet flue gas CO2 capture
Boyd, P. G., Chidambaram, A., García-Díez, E., Ireland, C. P., Daff, T. D., Bounds, R., Gładysiak, A., Schouwink, P., Moosavi, S. M., Maroto-Valer, M. M., et al. (2019) · Nature · doi:10.1038/s41586-019-1798-7 · Cited by 885 · see citing works
Electron Paramagnetic Resonance of d Transition Metal Compounds
(1992) · Studies in inorganic chemistry · doi:10.1016/c2009-0-09084-9 · Cited by 867 · see citing works
Hubbard-corrected DFT energy functionals: The LDA+U description of correlated systems
Himmetoḡlu, B., Floris, A., de Gironcoli, S., & Cococcioni, M. (2013) · International Journal of Quantum Chemistry · doi:10.1002/qua.24521 · Cited by 867 · see citing works
Chung, Y. G., Haldoupis, E., Bucior, B. J., Haranczyk, M., Lee, S., Zhang, H., Vogiatzis, K. D., Milisavljevic, M., Ling, S., Camp, J. S., et al. (2019) · Journal of Chemical & Engineering Data · doi:10.1021/acs.jced.9b00835 · Cited by 863 · see citing works
Hansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815 · see citing works
Accelerated search for materials with targeted properties by adaptive design
Xue, D., Balachandran, P. V., Hogden, J., Theiler, J. P., Xue, D., & Lookman, T. (2016) · Nature Communications · doi:10.1038/ncomms11241 · Cited by 801 · see citing works
Bacon, A. D., & Zerner, M. C. (1979) · Theoretical Chemistry Accounts · doi:10.1007/bf00547605 · Cited by 796 · see citing works
Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms
Bergstra, J., Yamins, D., & Cox, D. (2013) · Proceedings of the Python in Science Conferences · doi:10.25080/majora-8b375195-003 · Cited by 784 · see citing works
Nicewicz, D. A., Roth, H. G., & Romero, N. A. (2015) · Synlett · doi:10.1055/s-0035-1561297 · Cited by 776 · see citing works
Prediction of Organic Reaction Outcomes Using Machine Learning
Coley, C. W., Barzilay, R., Jaakkola, T., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00064 · Cited by 755 · see citing works
Phosphorus ligand exchange equilibriums on zerovalent nickel. Dominant role for steric effects
Tolman, C. A. (1970) · Journal of the American Chemical Society · doi:10.1021/ja00713a007 · Cited by 754 · see citing works
Chung, Y. G., Camp, J. S., Harańczyk, M., Sikora, B. J., Bury, W., Krungleviciute, V., Yildirim, T., Farha, O. K., Sholl, D. S., & Snurr, R. Q. (2014) · Chemistry of Materials · doi:10.1021/cm502594j · Cited by 748 · see citing works
Many-electron self-interaction error in approximate density functionals
Mori‐Sánchez, P., Cohen, A. J., & Yang, W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2403848 · Cited by 741 · see citing works
Combinatorial screening for new materials in unconstrained composition space with machine learning
Meredig, B., Agrawal, A., Kirklin, S. J., Saal, J. E., Doak, J. W., Thompson, A. E., Zhang, K., Choudhary, A., & Wolverton, C. M. (2014) · Physical Review B · doi:10.1103/physrevb.89.094104 · Cited by 736 · see citing works
The Chemistry of Redox‐Flow Batteries
Noack, J. N., Roznyatovskaya, N. V., Herr, T., & Fischer, P. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201410823 · Cited by 732 · see citing works
Design of New Materials for Methane Storage
Düren, T., Sarkisov, L., Yaghi, O. M., & Snurr, R. Q. (2004) · Langmuir · doi:10.1021/la0355500 · Cited by 731 · see citing works
First-principles LDA + U and GGA + U study of cerium oxides: Dependence on the effective U parameter
Loschen, C., Carrasco, J., Neyman, K. M., & Illas, F. (2007) · Physical Review B · doi:10.1103/physrevb.75.035115 · Cited by 724 · see citing works
Raynal, M., Ballester, P., Vidal‐Ferran, A., & van Leeuwen, P. W. N. M. (2013) · Chemical Society Reviews · doi:10.1039/c3cs60027k · Cited by 719 · see citing works
Pierpont, C. G. (2001) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(01)00309-5 · Cited by 686 · see citing works
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Zahrt, A. F., Henle, J. J., Rose, B. T., Wang, Y., Darrow, W. T., & Denmark, S. E. (2019) · Science · doi:10.1126/science.aau5631 · Cited by 683 · see citing works
Hydroformylation of alkenes by use of rhodium complex catalysts
Evans, D., Osborn, J. A., & Wilkinson, G. R. (1968) · Journal of the Chemical Society A Inorganic Physical Theoretical · doi:10.1039/j19680003133 · Cited by 682 · see citing works
Density Functional Theory in Transition-Metal Chemistry: A Self-Consistent Hubbard U Approach
Kulik, H. J., Cococcioni, M., Scherlis, D. A., & Marzari, N. (2006) · Physical Review Letters · doi:10.1103/physrevlett.97.103001 · Cited by 670 · see citing works
Self-citationSmith, J. S., Nebgen, B. T., Zubatyuk, R. I., Lubbers, N., Devereux, C., Barros, K. M., Tretiak, S., Isayev, O., & Roitberg, A. E. (2019) · Nature Communications · doi:10.1038/s41467-019-10827-4 · Cited by 661 · see citing works
Universal fragment descriptors for predicting properties of inorganic crystals
Isayev, O., Oses, C., Toher, C., Gossett, E., Stefano Curtarolo, A., & Tropsha, A. (2017) · Nature Communications · doi:10.1038/ncomms15679 · Cited by 638 · see citing works
Organic synthesis in a modular robotic system driven by a chemical programming language
Steiner, S., Wolf, J. B., Glatzel, S., Andreou, A., Granda, J. M., Keenan, G., Hinkley, T., Aragón-Camarasa, G., Kitson, P. J., Angelone, D., et al. (2018) · Science · doi:10.1126/science.aav2211 · Cited by 630 · see citing works
Geometries of Transition-Metal Complexes from Density-Functional Theory
Bühl, M., & Kabrede, H. (2006) · Journal of Chemical Theory and Computation · doi:10.1021/ct6001187 · Cited by 628 · see citing works
Understanding the diversity of the metal-organic framework ecosystem
Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020) · Nature Communications · doi:10.1038/s41467-020-17755-8 · Cited by 622 · see citing works
Self-citationCoronado, E., & Espallargas, G. M. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35278h · Cited by 619 · see citing works
Ulissi, Z. W., Medford, A. J., Bligaard, T., & Nørskov, J. K. (2017) · Nature Communications · doi:10.1038/ncomms14621 · Cited by 614 · see citing works
Swain, M. C., & Cole, J. M. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00207 · Cited by 598 · see citing works
Cobalt(ii) single-molecule magnets
Murrie, M. (2010) · Chemical Society Reviews · doi:10.1039/b913279c · Cited by 592 · see citing works
Evaluation of Homogeneous Electrocatalysts by Cyclic Voltammetry
Rountree, E. S., McCarthy, B. D., Eisenhart, T. T., & Dempsey, J. L. (2014) · Inorganic Chemistry · doi:10.1021/ic500658x · Cited by 591 · see citing works
AiiDA: automated interactive infrastructure and database for computational science
Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N., & Kozinsky, B. (2015) · Computational Materials Science · doi:10.1016/j.commatsci.2015.09.013 · Cited by 584 · see citing works
Degree of Rate Control: How Much the Energies of Intermediates and Transition States Control Rates
Stegelmann, C., Andreasen, A., & Campbell, C. T. (2009) · Journal of the American Chemical Society · doi:10.1021/ja9000097 · Cited by 584 · see citing works
Jencks, W. P. (1985) · Chemical Reviews · doi:10.1021/cr00070a001 · Cited by 550 · see citing works
FireWorks: a dynamic workflow system designed for high‐throughput applications
Jain, A., Ong, S. P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G., Hautier, G., et al. (2015) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3505 · Cited by 548 · see citing works
Trans-effects in octahedral transition metal complexes
Coe, B. J., & Glenwright, S. J. (2000) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(99)00184-8 · Cited by 543 · see citing works
Spectra of Transition-Metal Complexes
Orgel, L. E. (1955) · The Journal of Chemical Physics · doi:10.1063/1.1742182 · Cited by 540 · see citing works
Transition metal and organometallic anion complexation agents
Beer, P. D., & Hayes, E. J. (2002) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(02)00303-x · Cited by 535 · see citing works
The bite angle makes the difference: a practical ligand parameter for diphosphine ligands
Dierkes, P., & van Leeuwen, P. W. N. M. (1999) · Journal of the Chemical Society Dalton Transactions · doi:10.1039/a807799a · Cited by 535 · see citing works
Electronic structure and bonding in methyl- and perfluoromethyl(pentacarbonyl)manganese
Hall, M. B., & Fenske, R. F. (1972) · Inorganic Chemistry · doi:10.1021/ic50110a022 · Cited by 531 · see citing works
Geometries of Third-Row Transition-Metal Complexes from Density-Functional Theory
Bühl, M., Reimann, C., Pantazis, D. A., Bredow, T., & Neese, F. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct800172j · Cited by 524 · see citing works
Computational electrochemistry: prediction of liquid-phase reduction potentials
Marenich, A. V., Ho, J., Coote, M. L., Cramer, C. J., & Truhlar, D. G. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp01572j · Cited by 511 · see citing works
Understanding trends in C–H bond activation in heterogeneous catalysis
Latimer, A. A., Kulkarni, A. R., Aljama, H. A., Montoya, J. H., Yoo, J. S., Tsai, C., Abild‐Pedersen, F., Studt, F., & Nørskov, J. K. (2016) · Nature Materials · doi:10.1038/nmat4760 · Cited by 499 · see citing works
Reconfigurable system for automated optimization of diverse chemical reactions
Bédard, A., Adamo, A., Aroh, K. C., Russell, M. G., Bedermann, A. A., Torosian, J., Yue, B., Jensen, K. F., & Jamison, T. F. (2018) · Science · doi:10.1126/science.aat0650 · Cited by 488 · see citing works
Álvarez, S., Avnir, D., Llunell, M., & Pinsky, M. B. (2002) · New Journal of Chemistry · doi:10.1039/b200641n · Cited by 486 · see citing works
Hillier, A. C., Sommer, W., Yong, B. S., Petersen, J. L., Cavallo, L., & Nolan, S. P. (2003) · Organometallics · doi:10.1021/om034016k · Cited by 483 · see citing works
Transition metal complexes as molecular machine prototypes
Champin, B., Mobian, P., & Sauvage, J. (2006) · Chemical Society Reviews · doi:10.1039/b604484k · Cited by 478 · see citing works
Gómez‐Coca, S., Cremades, E., Aliaga‐Alcalde, N., & Ruíz, E. (2013) · Journal of the American Chemical Society · doi:10.1021/ja4015138 · Cited by 477 · see citing works
Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
Kim, E., Huang, K., Saunders, A., McCallum, A., Ceder, G., & Olivetti, E. (2017) · Chemistry of Materials · doi:10.1021/acs.chemmater.7b03500 · Cited by 473 · see citing works
Density Functionals for Inorganometallic and Organometallic Chemistry
Schultz, N. E., Zhao, Y., & Truhlar, D. G. (2005) · The Journal of Physical Chemistry A · doi:10.1021/jp0539223 · Cited by 472 · see citing works
Poli, R., & Harvey, J. N. (2002) · Chemical Society Reviews · doi:10.1039/b200675h · Cited by 470 · see citing works
Application of a universal force field to metal complexes
Rappé, A. K., Colwell, K. S., & Casewit, C. J. (1993) · Inorganic Chemistry · doi:10.1021/ic00068a012 · Cited by 456 · see citing works
Karton, A., Daon, S., & Martin, J. M. L. (2011) · Chemical Physics Letters · doi:10.1016/j.cplett.2011.05.007 · Cited by 451 · see citing works
The angular overlap model, an attempt to revive the ligand field approaches
Schäffer, C. E., & Jørgensen, C. K. (1965) · Molecular Physics · doi:10.1080/00268976500100551 · Cited by 449 · see citing works
Reiher, M. (2002) · Inorganic Chemistry · doi:10.1021/ic025891l · Cited by 442 · see citing works
Optimizing Chemical Reactions with Deep Reinforcement Learning
Zhou, Z., Li, X., & Zare, R. N. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00492 · Cited by 437 · see citing works
Millisecond Coherence Time in a Tunable Molecular Electronic Spin Qubit
Zadrozny, J. M., Niklas, J., Poluektov, O. G., & Freedman, D. E. (2015) · ACS Central Science · doi:10.1021/acscentsci.5b00338 · Cited by 436 · see citing works
Neural Networks for the Prediction of Organic Chemistry Reactions
Wei, J. N., Duvenaud, D., & Aspuru‐Guzik, A. (2016) · ACS Central Science · doi:10.1021/acscentsci.6b00219 · Cited by 436 · see citing works
Electronic tuning of asymmetric catalysts
Jacobsen, E. N., Zhang, W., & Güler, M. L. (1991) · Journal of the American Chemical Society · doi:10.1021/ja00017a069 · Cited by 435 · see citing works
Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities
Vogiatzis, K. D., Polynski, M. V., Kirkland, J. K., Townsend, J., Hashemi, A., Liu, C., & Pidko, E. A. (2018) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00361 · Cited by 428 · see citing works
Green, M. L. H., Marder, S. R., Thompson, M. E., Bandy, J. A., Bloor, D., Kolinsky, P. V., & Jones, R. J. (1987) · Nature · doi:10.1038/330360a0 · Cited by 423 · see citing works
Discovering chemistry with an ab initio nanoreactor
Wang, L., Titov, A., McGibbon, R. T., Liu, F., Pande, V. S., & Martı́nez, T. J. (2014) · Nature Chemistry · doi:10.1038/nchem.2099 · Cited by 423 · see citing works
Self-citationPredicting Crystal Structures with Data Mining of Quantum Calculations
Stefano Curtarolo, A., Morgan, D. D., Persson, K. A., Rodgers, J., & Ceder, G. (2003) · Physical Review Letters · doi:10.1103/physrevlett.91.135503 · Cited by 418 · see citing works
Zerner, M. C., Gouterman, M., & Kobayashi, H. (1966) · Theoretical Chemistry Accounts · doi:10.1007/bf00528464 · Cited by 412 · see citing works
Computational Ligand Descriptors for Catalyst Design
Durand, D. J., & Fey, N. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00588 · Cited by 406 · see citing works
Validation of Exchange−Correlation Functionals for Spin States of Iron Complexes
Swart, M., Groenhof, A. R., Ehlers, A. W., & Lammertsma, K. (2004) · The Journal of Physical Chemistry A · doi:10.1021/jp049043i · Cited by 405 · see citing works
Baik, M., & Friesner, R. A. (2002) · The Journal of Physical Chemistry A · doi:10.1021/jp025853n · Cited by 404 · see citing works
Predictive and mechanistic multivariate linear regression models for reaction development
Santiago, C. B., Guo, J.-Y., & Sigman, M. S. (2018) · Chemical Science · doi:10.1039/c7sc04679k · Cited by 403 · see citing works
Inverse design of nanoporous crystalline reticular materials with deep generative models
Yao, Z., Sánchez-Lengeling, B., Bobbitt, N. S., Bucior, B. J., Kumar, S. G. H., Collins, S. P., Burns, T. D., Woo, T. K., Farha, O. K., Snurr, R. Q., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00271-1 · Cited by 402 · see citing works
Jonas, V., & Thiel, W. (1995) · The Journal of Chemical Physics · doi:10.1063/1.468839 · Cited by 399 · see citing works
Deactivation in Homogeneous Transition Metal Catalysis: Causes, Avoidance, and Cure
Crabtree, R. H. (2014) · Chemical Reviews · doi:10.1021/cr5004375 · Cited by 399 · see citing works
Preface: Forum on Redox-Active Ligands
Chirik, P. J. (2011) · Inorganic Chemistry · doi:10.1021/ic201881k · Cited by 398 · see citing works
Organometallic Single-Molecule Magnets
Layfield, R. A. (2014) · Organometallics · doi:10.1021/om401107f · Cited by 398 · see citing works
Genetic algorithms in chemometrics and chemistry: a review
Leardi, R. (2001) · Journal of Chemometrics · doi:10.1002/cem.651 · Cited by 397 · see citing works
Metal Ion Modeling Using Classical Mechanics
Li, P., & Merz, K. M. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00440 · Cited by 395 · see citing works
Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning
Yuan, R., Liu, Z., Balachandran, P. V., Xue, D., Zhou, Y., Ding, X., Sun, J., Xue, D., & Lookman, T. (2018) · Advanced Materials · doi:10.1002/adma.201702884 · Cited by 395 · see citing works
Room temperature quantum coherence in a potential molecular qubit
Bader, K., Dengler, D., Lenz, S., Endeward, B., Jiang, S., Neugebauer, P., & van Slageren, J. (2014) · Nature Communications · doi:10.1038/ncomms6304 · Cited by 389 · see citing works
Dyson, P. J., & Jessop, P. G. (2016) · Catalysis Science & Technology · doi:10.1039/c5cy02197a · Cited by 382 · see citing works
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379 · see citing works
Nonlinear optical properties of transition metal acetylides and their derivatives
Powell, C. E., & Humphrey, M. G. (2004) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2004.03.009 · Cited by 377 · see citing works
Toward computational screening in heterogeneous catalysis: Pareto-optimal methanation catalysts
Andersson, M., Bligaard, T., Kustov, A. L., Larsen, K. E., Greeley, J., Johannessen, T., Christensen, C. H., & Nørskov, J. K. (2006) · Journal of Catalysis · doi:10.1016/j.jcat.2006.02.016 · Cited by 365 · see citing works
Autonomous Discovery in the Chemical Sciences Part I: Progress
Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 365 · see citing works
Chen, P. (2003) · Angewandte Chemie International Edition · doi:10.1002/anie.200200560 · Cited by 363 · see citing works
Anantharaj, S., & Aravindan, V. (2019) · Advanced Energy Materials · doi:10.1002/aenm.201902666 · Cited by 359 · see citing works
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SELFIES and the future of molecular string representations
Krenn, M., Ai, Q., Barthel, S., Carson, N., Frei, A., Frey, N. C., Friederich, P., Gaudin, T., Gayle, A. A., Jablonka, K. M., et al. (2022) · Patterns · doi:10.1016/j.patter.2022.100588 · Cited by 268 · see citing works
Zhu, Y., Tang, Z., Yuan, L., Li, B., Shao, Z., & Guo, W. (2024) · Chemical Society Reviews · doi:10.1039/d3cs01020a · Cited by 220 · see citing works
Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction
Li, J., Wu, N., Zhang, J., Wu, H., Pan, K., Wang, Y., Liu, G., Liu, X., Yao, Z., & Zhang, Q. (2023) · Nano-Micro Letters · doi:10.1007/s40820-023-01192-5 · Cited by 211 · see citing works
Benavides-Hernández, J., & Dumeignil, F. (2024) · ACS Catalysis · doi:10.1021/acscatal.3c06293 · Cited by 208 · see citing works
Segev, G., Kibsgaard, J., Hahn, C., Xu, Z. J., Cheng, W.-H. S., Deutsch, T. G., Xiang, C., Zhang, J., Hammarström, L., Nocera, D. G., et al. (2022) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/ac6f97 · Cited by 153 · see citing works
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
Majumdar, S., Moosavi, S. M., Jablonka, K. M., Ongari, D., & Smit, B. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c16220 · Cited by 141 · see citing works
Tu, Z., Stuyver, T., & Coley, C. W. (2022) · Chemical Science · doi:10.1039/d2sc05089g · Cited by 141 · see citing works
Synthesis optimization and adsorption modeling of biochar for pollutant removal via machine learning
Zhang, W., Chen, R., Li, J., Huang, T., Wu, B., Ma, J., Wen, Q., Tan, J., & Huang, W. (2023) · Biochar · doi:10.1007/s42773-023-00225-x · Cited by 125 · see citing works
Machine learning of material properties: Predictive and interpretable multilinear models
Allen, A. E. A., & Tkatchenko, A. (2022) · Science Advances · doi:10.1126/sciadv.abm7185 · Cited by 122 · see citing works
Dotson, J. J., van Dijk, L., Timmerman, J. C., Grosslight, S., Walroth, R. C., Gosselin, F., Püntener, K., Mack, K. A., & Sigman, M. S. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c08513 · Cited by 112 · see citing works
Nandy, A., Duan, C., & Kulik, H. J. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100778 · Cited by 103 · see citing works
Self-citationUnraveling the Contributions to Spin–Lattice Relaxation in Kramers Single-Molecule Magnets
Mondal, S., & Lunghi, A. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c08876 · Cited by 94 · see citing works
Lunghi, A., & Sanvito, S. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00424-3 · Cited by 90 · see citing works
Xu, L., Wu, F., Chen, R., & Li, L. (2023) · Energy storage materials · doi:10.1016/j.ensm.2023.102785 · Cited by 89 · see citing works
Optical sorting: past, present and future
Yang, M., Shi, Y., Song, Q., Wei, Z., Dun, X., Wang, Z. M., Wang, Z., Qiu, C., Zhang, H., & Cheng, X. (2025) · Light Science & Applications · doi:10.1038/s41377-024-01734-5 · Cited by 82 · see citing works
An overview of advancement of organoruthenium(II) complexes as prospective anticancer agents
Bashir, M., Mantoo, I. A., Arjmand, F., Tabassum, S., & Yousuf, I. (2023) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2023.215169 · Cited by 81 · see citing works
Metal–insulator–semiconductor photoelectrodes for enhanced photoelectrochemical water splitting
Wei, S., Xia, X., Bi, S., Hu, S., Wu, X., Hsu, H., Zou, X., Huang, K., Zhang, D. W., Sun, Q., et al. (2024) · Chemical Society Reviews · doi:10.1039/d3cs00820g · Cited by 78 · see citing works
MatGPT: A Vane of Materials Informatics from Past, Present, to Future
Wang, Z., Chen, A., Tao, K., Han, Y., & Li, J. (2023) · Advanced Materials · doi:10.1002/adma.202306733 · Cited by 75 · see citing works
Nandy, A., Duan, C., Goffinet, C., & Kulik, H. J. (2022) · JACS Au · doi:10.1021/jacsau.2c00176 · Cited by 74 · see citing works
Self-citationModeling of Nanomaterials for Supercapacitors: Beyond Carbon Electrodes
Bi, S., Knijff, L., Lian, X., van Hees, A., Zhang, C., & Salanne, M. (2024) · ACS Nano · doi:10.1021/acsnano.4c01787 · Cited by 74 · see citing works
Machine Learning for the Discovery, Design, and Engineering of Materials.
Duan, C., Nandy, A., & Kulik, H. J. (2022) · Annual Review of Chemical and Biomolecular Engineering · doi:10.1146/annurev-chembioeng-092320-120230 · Cited by 69 · see citing works
Self-citationAutonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis
Steiner, M., & Reiher, M. (2022) · Topics in Catalysis · doi:10.1007/s11244-021-01543-9 · Cited by 66 · see citing works
Unlocking the computational design of metal–organic cages
Tarzia, A., & Jelfs, K. E. (2022) · Chemical Communications · doi:10.1039/d2cc00532h · Cited by 66 · see citing works
On-demand reverse design of polymers with PolyTAO
Qiu, H., & Sun, Z. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01466-5 · Cited by 65 · see citing works
Machine learning for CO 2 capture and conversion: A review
Jerng, S. E., Park, Y. J., & Li, J. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100361 · Cited by 64 · see citing works
Open-Source Machine Learning in Computational Chemistry
Hagg, A., & Kirschner, K. N. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00643 · Cited by 62 · see citing works
Deep learning metal complex properties with natural quantum graphs
Kneiding, H., Lukin, R., Lang, L., Reine, S., Pedersen, T. B., De Bin, R., & Balcells, D. (2023) · Digital Discovery · doi:10.1039/d2dd00129b · Cited by 61 · see citing works
High-entropy alloy catalysts: high-throughput and machine learning-driven design
Chen, L., Chen, Z., Yao, X., Su, B., Chen, W., Pang, X., Kim, K.-S., Singh, C. V., & Zou, Y. (2022) · Journal of Materials Informatics · doi:10.20517/jmi.2022.23 · Cited by 59 · see citing works
Arun, A. P., Sreenivasan, N., Patil, J. H., Kusanur, R. A., Ramachandraiah, H. L., & Ramakrishna, M. (2025) · Processes · doi:10.3390/pr13123846 · Cited by 58 · see citing works
A Look Inside the Black Box of Machine Learning Photodynamics Simulations
Li, J., & Lopez, S. A. (2022) · Accounts of Chemical Research · doi:10.1021/acs.accounts.2c00288 · Cited by 57 · see citing works
Allen, A. E. A., Lubbers, N., Matin, S., Smith, J. S., Messerly, R. A., Tretiak, S., & Barros, K. M. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01339-x · Cited by 55 · see citing works
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
Zhu, K., Trizio, E., Zhang, J., Hu, R., Jiang, L., Hou, T., & Bonati, L. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00700 · Cited by 54 · see citing works
Computational screening of transition metal-doped CdS for photocatalytic hydrogen production
Li, Y., Bahamón, D., Sinnokrot, M. O., & Vega, L. F. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00922-4 · Cited by 53 · see citing works
AI-driven advances in metal–organic frameworks: from data to design and applications
Song, Y., Li, J., Chi, D., Xu, Z., Liu, J., Chen, M., & Wang, Z. (2025) · Chemical Communications · doi:10.1039/d5cc04220h · Cited by 52 · see citing works
Li, S., Jansone‐Popova, S., & Jiang, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-62074-3 · Cited by 51 · see citing works
Engineering of abundant metal complexes for electrochemical water splitting
Kousar, N., Patil, G., Kumbara, A. C., Nisty, B., H., R. G., & Sannegowda, L. K. (2025) · Dalton Transactions · doi:10.1039/d5dt01438g · Cited by 51 · see citing works
Quantum chemical calculations for reaction prediction in the development of synthetic methodologies
Hayashi, H., Maeda, S., & Mita, T. (2023) · Chemical Science · doi:10.1039/d3sc03319h · Cited by 49 · see citing works
Spin-Phonon Relaxation in Magnetic Molecules: Theory, Predictions and Insights
Lunghi, A. (2023) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-031-31038-6_6 · Cited by 47 · see citing works
Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules
Xie, Z., Evangelopoulos, X., Omar, Ö. H., Troisi, A., Cooper, A. I., & Chen, L. (2023) · Chemical Science · doi:10.1039/d3sc04610a · Cited by 47 · see citing works
Nandy, A., Adamji, H., Kastner, D. W., Vennelakanti, V., Nazemi, A., Liu, M., & Kulik, H. J. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c02096 · Cited by 46 · see citing works
Self-citationGinovska, B., Gutiérrez, O. Y., Karkamkar, A., Lee, M., Lercher, J. A., Liu, Y., Raugei, S., Rousseau, R. J., & Shaw, W. J. (2023) · ACS Catalysis · doi:10.1021/acscatal.3c00320 · Cited by 45 · see citing works
A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes
Strandgaard, M., Linjordet, T., Kneiding, H., Burnage, A. L., Nova, A., Jensen, J. H., & Balcells, D. (2025) · JACS Au · doi:10.1021/jacsau.5c00242 · Cited by 45 · see citing works
Optimal transport for generating transition states in chemical reactions
Duan, C., Liu, G.-H., Du, Y., Chen, T., Zhao, Q., Jia, H., Gomes, C. P., Theodorou, E. A., & Kulik, H. J. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01010-0 · Cited by 43 · see citing works
Self-citationDirectional multiobjective optimization of metal complexes at the billion-system scale
Kneiding, H., Nova, A., & Balcells, D. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00616-5 · Cited by 42 · see citing works
Machine learning spectroscopy to advance computation and analysis
Westermayr, J., & Marquetand, P. (2025) · Chemical Science · doi:10.1039/d5sc05628d · Cited by 39 · see citing works
Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies
Edaugal, J. P., Zhang, D., Liu, D., Glezakou, V., & Sun, N. (2025) · Chem & Bio Engineering · doi:10.1021/cbe.4c00170 · Cited by 38 · see citing works
Cyclic iron tetra N-heterocyclic carbenes: synthesis, properties, reactivity, and catalysis
Schlachta, T. P., & Kühn, F. E. (2023) · Chemical Society Reviews · doi:10.1039/d2cs01064j · Cited by 38 · see citing works
OSCAR: an extensive repository of chemically and functionally diverse organocatalysts
Gallarati, S., van Gerwen, P., Laplaza, R., Vela, S., Fabrizio, A., & Corminbœuf, C. (2022) · Chemical Science · doi:10.1039/d2sc04251g · Cited by 37 · see citing works
Lü, J., Song, Z., Zhao, Q., Du, Y., Cao, Y., Jia, H., & Duan, C. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c02097 · Cited by 36 · see citing works
Self-citationSelection of Low-Dimensional 3-D Geometric Descriptors for Accurate Enantioselectivity Prediction
Antinucci, G., Dereli, B., Vittoria, A., Budzelaar, P. H. M., Cipullo, R., Goryunov, G. P., Kulyabin, P. S., Uborsky, D. V., Cavallo, L., Ehm, C., et al. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c00976 · Cited by 36 · see citing works
Paving the road towards automated homogeneous catalyst design
Kalikadien, A. V., Mirza, A., Hossaini, A. N., Sreenithya, A., & Pidko, E. A. (2024) · ChemPlusChem · doi:10.1002/cplu.202300702 · Cited by 35 · see citing works
Radoń, M., Drabik, G., Hodorowicz, M. A., & Szklarzewicz, J. (2024) · Chemical Science · doi:10.1039/d4sc05471g · Cited by 35 · see citing works
Design and Application of Electrocatalyst Based on Machine Learning
Gu, Y., Zhang, H., Xu, Z., Ren, R., Kong, X., Wang, Y., Zhu, H., Xue, D., Zhang, Y., Ma, Y., et al. (2025) · Interdisciplinary materials · doi:10.1002/idm2.12249 · Cited by 35 · see citing works
Hayashi, H., Katsuyama, H., Takano, H., Harabuchi, Y., Maeda, S., & Mita, T. (2022) · Nature Synthesis · doi:10.1038/s44160-022-00128-y · Cited by 35 · see citing works
AI in single-atom catalysts: a review of design and applications
Yu, Q., Ma, N., Leung, C., Liu, H., Ren, Y., & Wei, Z. (2025) · Journal of Materials Informatics · doi:10.20517/jmi.2024.78 · Cited by 32 · see citing works
Machine learning accelerated photodynamics simulations
Li, J., & Lopez, S. A. (2023) · Chemical Physics Reviews · doi:10.1063/5.0159247 · Cited by 32 · see citing works
When Metal Nanoclusters Meet Smart Synthesis
Yang, Z., Shi, A., Zhang, R., Ji, Z., Li, J., Lyu, J., Qian, J., Chen, T., Wang, X., You, F., et al. (2024) · ACS Nano · doi:10.1021/acsnano.4c09597 · Cited by 31 · see citing works
Duan, C., Nandy, A., Meyer, R., Arunachalam, N., & Kulik, H. J. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00384-0 · Cited by 31 · see citing works
Self-citationChen, X., Li, P., Hruška, E., & Liu, F. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00506b · Cited by 30 · see citing works
Self-citationA data-driven approach for the guided regulation of exposed facets in nanoparticles
Ye, Z., Shen, B., Kang, D., Shen, J., Huang, J. S., Wang, Z., Huang, L., Wolverton, C., & Mirkin, C. A. (2024) · Nature Synthesis · doi:10.1038/s44160-024-00561-1 · Cited by 29 · see citing works
Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning
Macé, S., Xu, Y., & Nguyen, B. N. (2024) · ChemCatChem · doi:10.1002/cctc.202301475 · Cited by 29 · see citing works
Gao, Y., Hu, K., Rao, J., Zhu, Q., & Liao, K. (2024) · ACS Catalysis · doi:10.1021/acscatal.4c04277 · Cited by 28 · see citing works
Best practices in catalysis: A perspective
Bhan, A., & Delgass, W. N. (2021) · Journal of Catalysis · doi:10.1016/j.jcat.2021.12.014 · Cited by 28 · see citing works
Shehnaz, Siddiqui, W. A., Ashraf, A., Ashfaq, M. H., Tahir, M. N., & Niaz, S. (2023) · Applied Organometallic Chemistry · doi:10.1002/aoc.7077 · Cited by 28 · see citing works
Neugebauer, H., Bädorf, B., Ehlert, S., Hansen, A., & Grimme, S. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27185 · Cited by 28 · see citing works
Unsleber, J. P., Liu, H., Talirz, L., Weymuth, T., Mörchen, M., Grofe, A., Wecker, D., Stein, C. J., Panyala, A., Peng, B., et al. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0136526 · Cited by 26 · see citing works
Kalikadien, A. V., Valsecchi, C., van Putten, R., Maes, T., Muuronen, M., Dyubankova, N., Lefort, L., & Pidko, E. A. (2024) · Chemical Science · doi:10.1039/d4sc03647f · Cited by 26 · see citing works
Pultar, F., Thürlemann, M., Gordiy, I., Doloszeski, E., & Riniker, S. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.4c17015 · Cited by 25 · see citing works
Vittoria, A., Urciuoli, G., Costanzo, S., Tammaro, D., Cannavacciuolo, F. D., Pasquino, R., Cipullo, R., Auriemma, F., Grizzuti, N., Maffettone, P. L., et al. (2022) · Macromolecules · doi:10.1021/acs.macromol.2c00813 · Cited by 25 · see citing works
Machine learning for multiscale modeling in computational molecular design
Alshehri, A. S., & You, F. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100752 · Cited by 25 · see citing works
Advancements and prospects of deep learning in biomaterials evolution
You, S., Fan, Y., Chen, Y., Jiang, X., Liu, W., Zhou, X.-Y., Zhang, J., Zheng, J., Yang, H., & Hou, X. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102116 · Cited by 24 · see citing works
Kareem, S. A., Ibrahim, M. A., Anaele, J. U., Olanrewaju, O. F., Aikulola, E. O., & Bodunrin, M. O. (2025) · Next Materials · doi:10.1016/j.nxmate.2025.100864 · Cited by 24 · see citing works
Graph neural networks for predicting metal–ligand coordination of transition metal complexes
Toney, J., Michel, R. G. S., Garrison, A. G., Kevlishvili, I., & Kulik, H. J. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415658122 · Cited by 24 · see citing works
Self-citationSun, J., Yao, C., Xiang, H., Chen, F., Lu, J., & Huang, W. (2025) · Advanced Materials · doi:10.1002/adma.202514182 · Cited by 23 · see citing works
Matsuoka, W., Harabuchi, Y., & Maeda, S. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c00267 · Cited by 23 · see citing works
Chen, S., Meyer, Z., Jensen, B., Kraus, A., Lambert, A., & Ess, D. H. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01310 · Cited by 23 · see citing works
Khan, I. M., Niazi, S., Yue, L., Zhang, Y., Pasha, I., Khan, M. K. I., Akhtar, W., Mohsin, A., Chughati, M. F. J., & Wang, Z. (2022) · Talanta · doi:10.1016/j.talanta.2022.123228 · Cited by 23 · see citing works
Zhu, W., Wu, J., Kang, Y., & Xue, P. (2025) · Advanced Materials · doi:10.1002/adma.202502407 · Cited by 22 · see citing works
Zhao, Q., Han, Y., Zhang, D., Wang, J., Zhong, P., Cui, T., Yin, B., Cao, Y., Jia, H., & Duan, C. (2025) · Advanced Science · doi:10.1002/advs.202506240 · Cited by 22 · see citing works
Self-citationWorking at the interfaces of data science and synthetic electrochemistry
Alvarado, J. I. M., Meinhardt, J. M., & Lin, S. (2022) · Tetrahedron Chem · doi:10.1016/j.tchem.2022.100012 · Cited by 22 · see citing works
Wu, Z., Kundu, B. K., Kang, W., Mao, L., Zhang, S., Yuan, L., Guo, F., & Han, C. (2023) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2023.02.043 · Cited by 21 · see citing works
Flow chemistry as a tool for high throughput experimentation
Lyall-Brookes, G., Padgham, A. C., & Slater, A. G. (2025) · Digital Discovery · doi:10.1039/d5dd00129c · Cited by 20 · see citing works
Boros, E., Comba, P., Engle, J. W., Harriswangler, C., Lapi, S. E., Lewis, J. S., Mastroianni, S., Mirica, L. M., Platas‐Iglesias, C., Ramogida, C. F., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00641 · Cited by 20 · see citing works
Park, J., Chung, I., Jeong, H., Lee, D., & Yun, Y. (2024) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2024.124622 · Cited by 20 · see citing works
Mariano, L. A., Anh, N. V. H., Briganti, V., & Lunghi, A. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c14076 · Cited by 20 · see citing works
Iridium- and Palladium-Based Catalysts in the Pharmaceutical Industry
López, Ó., & Padrón, J. M. (2022) · Catalysts · doi:10.3390/catal12020164 · Cited by 20 · see citing works
Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review
Hassan, M., Shahzadi, S., & Kloczkowski, A. (2025) · Cancers · doi:10.3390/cancers17111828 · Cited by 19 · see citing works
Theoretical Perspectives in Organocatalysis
Melnyk, N., Iribarren, Í., Mates‐Torres, E., & Trujillo, C. (2022) · Chemistry - A European Journal · doi:10.1002/chem.202201570 · Cited by 19 · see citing works
Chen, P., Zhou, Y., He, J., Jiang, W., Li, J., Ni, H., Zhang, Q., & Lin, L. (2023) · Chemistry of Materials · doi:10.1021/acs.chemmater.3c00245 · Cited by 19 · see citing works
Kelly, H. R., Sreekumar, S., Manee, V., Cuomo, A. E., Newhouse, T. R., Batista, V. S., & Buono, F. G. (2024) · ACS Catalysis · doi:10.1021/acscatal.3c06230 · Cited by 19 · see citing works
OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion
Cornet, F., Benediktsson, B., Hastrup, B., Schmidt, M. N., & Bhowmik, A. (2024) · Digital Discovery · doi:10.1039/d4dd00099d · Cited by 19 · see citing works
Does Hessian Data Improve the Performance of Machine Learning Potentials?
Rodriguez, A., Smith, J. S., & Mendoza-Cortés, J. L. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00402 · Cited by 18 · see citing works
Rahbani, N., de Silva, P., & Baudrin, E. (2023) · ChemSusChem · doi:10.1002/cssc.202300482 · Cited by 18 · see citing works
Quantum chemical DFT-based adsorption mechanism of Pb(II) on a modified biochar
Xie, H., Hu, A., Xu, L., Ao, S., Zhang, T., Li, W., & Yang, Y. (2024) · Biomass Conversion and Biorefinery · doi:10.1007/s13399-024-05604-3 · Cited by 18 · see citing works
Duan, C., Nandy, A., Adamji, H., Román‐Leshkov, Y., & Kulik, H. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00331 · Cited by 18 · see citing works
Self-citationDuan, C., Ladera, A., Liu, J. C. L., Taylor, M. G., Ariyarathna, I. R., & Kulik, H. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00468 · Cited by 18 · see citing works
Self-citationKumar, A., Bishnoi, P., Parmar, A., Khalid, M., Ubaidullah, M., & Al‐Enizi, A. M. (2026) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2026.217870 · Cited by 17 · see citing works
Villares, M., Saunders, C. M., & Fey, N. (2024) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2024.100055 · Cited by 17 · see citing works
Fite, S., Wahab, A., Paenurk, E., Gross, Z., & Gershoni‐Poranne, R. (2022) · Journal of Physical Organic Chemistry · doi:10.1002/poc.4458 · Cited by 17 · see citing works
Challenges for Kinetics Predictions via Neural Network Potentials: A Wilkinson’s Catalyst Case
Staub, R., Gantzer, P., Harabuchi, Y., Maeda, S., & Varnek, A. A. (2023) · Molecules · doi:10.3390/molecules28114477 · Cited by 16 · see citing works
- Authors
- Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Kulik, Heather J.
- Venue
- Chemical Reviews
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-07-14
- Volume
- 121
- Issue
- 16
- Pages
- 9927-10000
- ISSN
- 0009-2665 (ISSN-L)
- 1520-6890
- OpenAlex
- W3181257149
- Cited by
- 358
- References
- 718
- Citations by year
- 2026: 80 · 2025: 101 · 2024: 64 · 2023: 55 · 2022: 51 · 2021: 7
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Chemical Reviews (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Defense Advanced Research Projects Agency (D18AP00039)
- American Association for the Advancement of Science (Marion Milligan Mason Award)
- Office of Naval Research (N00014-17-1-2956, N00014-18-1-2434, N00014-20-1-2150)
- U.S. Department of Energy (DE-NA0003965, DE-SC0012702, DE-SC0018096, DE-SC0019112)
- Burroughs Wellcome Fund (Career Award at the Scientific Interface 1010755)
- National Science Foundation (CBET-1704266, CBET-1846426, ECCS-1449291)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- transition metal complexes, high-throughput screening, machine learning, structure-property relationships, catalyst discovery, density functional theory, force fields, semiempirical calculations, multi-objective optimization, artificial intelligence, data scarcity, lead compound identification, design rules
- MeSH terms
- Machine Learning, Metals, Transition Elements, Coordination Complexes, High-Throughput Screening Assays
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