Enumeration of de novo inorganic complexes for chemical discovery and machine learning

Gugler, Stefan; Janet, Jon Paul; Kulik, Heather J.

doi:10.1039/c9me00069k

Cited by 43Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesMagnetism in coordination complexes

Enumeration of de novo inorganic complexes for chemical discovery and machine learning

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Gugler, Stefan, Janet, Jon Paul, Kulik, Heather J.·2020·Molecular Systems Design & Engineering

DOI 10.1039/c9me00069k

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Enumerated, de novo transition metal complexes have unique spin state properties and accelerate machine learning model training.

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Gugler et al. relaxed constraints on the octet rule to sample a wider array of chemical bonding in ligands while constraining octahedral complexes to no more than 13 heavy atoms.
“Gugler et al. (26) relaxed constraints on the octet rule to sample a wider array of chemical bonding in ligands while constraining octahedral complexes to no more than 13 heavy atoms (i.e., C, N, O, P, or S).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [26]
Incorporation of OHLDB compounds reduced errors of ML models (i.e., ANNs) on experimental (i.e., CSD) complexes by approximately 25%.
“Incorporation of OHLDB compounds reduced errors of ML models (i.e., ANNs) on experimental (i.e., CSD) complexes by approximately 25% (26,46).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [26]

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Density-functional thermochemistry. III. The role of exact exchange
Becke, A. D. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464913 · Cited by 103,767 · see citing works
Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density
Lee, C., Yang, W., & Parr, R. G. (1988) · Physical Review B · doi:10.1103/physrevb.37.785 · Cited by 100,874 · see citing works
Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields
Stephens, P. J., Devlin, F. J., Chabalowski, C. F., & Frisch, M. J. (1994) · The Journal of Physical Chemistry · doi:10.1021/j100096a001 · Cited by 23,329 · see citing works
Accurate spin-dependent electron liquid correlation energies for local spin density calculations: a critical analysis
Vosko, S. H., Wilk, L., & Nusair, M. (1980) · Canadian Journal of Physics · doi:10.1139/p80-159 · Cited by 20,807 · see citing works
Ab initio effective core potentials for molecular calculations. Potentials for the transition metal atoms Sc to Hg
Hay, P. J., & Wadt, W. R. (1985) · The Journal of Chemical Physics · doi:10.1063/1.448799 · Cited by 13,834 · 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
Visible Light Photoredox Catalysis with Transition Metal Complexes: Applications in Organic Synthesis
Prier, C. K., Rankic, D. A., & MacMillan, D. W. C. (2013) · Chemical Reviews · doi:10.1021/cr300503r · Cited by 9,590 · see citing works
Porphyrin-Sensitized Solar Cells with Cobalt (II/III)–Based Redox Electrolyte Exceed 12 Percent Efficiency
Yella, A., Lee, H., Tsao, H. N., Yi, C., Chandiran, A. K., Nazeeruddin, M. K., Diau, E. W., Yeh, C., Zakeeruddin, S. M., & Grätzel, M. (2011) · Science · doi:10.1126/science.1209688 · Cited by 5,970 · see citing works
Angewandte Chemie International Edition
Vazdar, K., Kunetskiy, R. A., Saame, J., Kaupmees, K., Leito, I., & Jahn, U. (2006) · Angewandte Chemie International Edition · doi:10.1002/(issn)1521-3773 · Cited by 5,242 · see citing works
Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere
Sterner, R. W., & Elser, J. J. (2002) · Medical Entomology and Zoology · Cited by 4,476 · 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
Ruthenium(II)-Catalyzed C–H Bond Activation and Functionalization
Arockiam, P. B., Bruneau, C., & Dixneuf, P. H. (2012) · Chemical Reviews · doi:10.1021/cr300153j · Cited by 2,745 · see citing works
Environmental chemistry of the elements
Bowen, H. J. M. (1979) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 2,726 · see citing works
Solar Synthesis: Prospects in Visible Light Photocatalysis
Schultz, D. M., & Yoon, T. P. (2014) · Science · doi:10.1126/science.1239176 · Cited by 2,587 · see citing works
The ChEMBL database in 2017
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J. K., Méndez, D., Mutowo, P., Atkinson, F. L., Bellis, L. J., Cibrián–Uhalte, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,351 · see citing works
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Zhang, L., Han, J., Wang, H., Car, R., & Weinan, E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.143001 · Cited by 2,267 · see citing works
Physical Chemistry Chemical Physics
(2026) · Physical Chemistry Chemical Physics · doi:10.1039/1463-9084/1999 · Cited by 2,176 · see citing works
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,087 · see citing works
Catalytic Functionalization of C(sp 2 )H and C(sp 3 )H Bonds by Using Bidentate Directing Groups
Rouquet, G., & Chatani, N. (2013) · Angewandte Chemie International Edition · doi:10.1002/anie.201301451 · Cited by 2,010 · 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
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921 · 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
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
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
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,435 · see citing works
Journal of Chemical Theory and Computation
(2008) · Journal of Chemical Theory and Computation · doi:10.1021/issn.1549-9618 · Cited by 1,210 · see citing works
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K. (2017) · Science Advances · doi:10.1126/sciadv.1603015 · Cited by 1,165 · see citing works
Triplet Harvesting with 100% Efficiency by Way of Thermally Activated Delayed Fluorescence in Charge Transfer OLED Emitters
Dias, F. B., Bourdakos, K. N., Jankus, V., Moss, K. C., Kamtekar, K. T., Bhalla, V., Santos, J., Bryce, M. R., & Monkman, A. P. (2013) · Advanced Materials · doi:10.1002/adma.201300753 · Cited by 1,005 · see citing works
Quantum Chemistry on Graphical Processing Units. 3. Analytical Energy Gradients, Geometry Optimization, and First Principles Molecular Dynamics
Ufimtsev, I. S., & Martı́nez, T. J. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9003004 · Cited by 924 · see citing works
Accelerating materials property predictions using machine learning
Pilania, G., Wang, C., Jiang, X., Rajasekaran, S., & Ramprasad, R. (2013) · Scientific Reports · doi:10.1038/srep02810 · Cited by 851 · see citing works
Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space
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
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778 · see citing works
Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725 · 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-citation
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651 · see citing works
Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments
Ren, F., Ward, L. T., Williams, T., Laws, K. J., Wolverton, C., Hattrick-Simpers, J. R., & Mehta, A. (2018) · Science Advances · doi:10.1126/sciadv.aaq1566 · Cited by 519 · see citing works
Machine learning in catalysis
Kitchin, J. R. (2018) · Nature Catalysis · doi:10.1038/s41929-018-0056-y · Cited by 481 · see citing works
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Yao, K., Herr, J. E., Toth, D. W., Mckintyre, R., & Parkhill, J. (2018) · Chemical Science · doi:10.1039/c7sc04934j · Cited by 475 · see citing works
Blue-Light Emission of Cu(I) Complexes and Singlet Harvesting
Czerwieniec, R., Yu, J., & Yersin, H. (2011) · Inorganic Chemistry · doi:10.1021/ic200811a · Cited by 469 · see citing works
Predicting the Band Gaps of Inorganic Solids by Machine Learning
Zhuo, Y., Tehrani, A. M., & Brgoch, J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00124 · Cited by 458 · see citing works
Machine learning for heterogeneous catalyst design and discovery
Goldsmith, B. R., Esterhuizen, J. A., Liu, J., Bartel, C. J., & Sutton, C. A. (2018) · AIChE Journal · doi:10.1002/aic.16198 · Cited by 446 · see citing works
Angewandte Chemie International Edition in English
(2018) · Angewandte Chemie International Edition in English · doi:10.1002/(issn)1521-3773a · Cited by 444 · 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
Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening
Ma, X., Li, Z., Achenie, L. E. K., & Xin, H. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b01660 · Cited by 422 · see citing works
Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit
O’Boyle, N. M., Morley, C., & Hutchison, G. (2008) · Chemistry Central Journal · doi:10.1186/1752-153x-2-5 · Cited by 412 · 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
A “Level–Shifting” method for converging closed shell Hartree–Fock wave functions
Saunders, V. R., & Hillier, I. H. (1973) · International Journal of Quantum Chemistry · doi:10.1002/qua.560070407 · Cited by 362 · see citing works
Virtual Exploration of the Small‐Molecule Chemical Universe below 160 Daltons
Fink, T., Bruggesser, H., & Reymond, J. (2005) · Angewandte Chemie International Edition · doi:10.1002/anie.200462457 · Cited by 356 · see citing works
Coordination and Organometallic Chemistry of Metal−NO Complexes
Hayton, T. W., Legzdins, P., & Sharp, W. B. (2002) · Chemical Reviews · doi:10.1021/cr000074t · Cited by 351 · see citing works
Synthesis, Structure, and Characterization of Dinuclear Copper(I) Halide Complexes with P^N Ligands Featuring Exciting Photoluminescence Properties
Zink, D. M., Bächle, M., Baumann, T., Nieger, M., Kühn, M., Wang, C., Klopper, W., Monkowius, U. V., Hofbeck, T., Yersin, H., et al. (2012) · Inorganic Chemistry · doi:10.1021/ic300979c · Cited by 351 · see citing works
Differences between the four halide ligands, and discussion remarks on trigonal-bipyramidal complexes, on oxidation states, and on diagonal elements of one-electron energy
Jørgensen, C. K. (1966) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(00)80170-8 · Cited by 348 · see citing works
Hierarchical modeling of molecular energies using a deep neural network
Lubbers, N., Smith, J. S., & Barros, K. M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011181 · Cited by 342 · see citing works
ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Scientific Data · doi:10.1038/sdata.2017.193 · Cited by 306 · see citing works
Stochastic Voyages into Uncharted Chemical Space Produce a Representative Library of All Possible Drug-Like Compounds
Virshup, A. M., Contreras-García, J., Wipf, P., Yang, W., & Beratan, D. N. (2013) · Journal of the American Chemical Society · doi:10.1021/ja401184g · Cited by 294 · see citing works
Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity
Huang, B., & von Lilienfeld, O. A. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4964627 · Cited by 294 · see citing works
Geometry optimization made simple with translation and rotation coordinates
Wang, L., & Song, C. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4952956 · Cited by 291 · see citing works
Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
Janet, J. P., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.7b08750 · Cited by 289 · see citing works
Self-citation
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
Meredig, B., Antono, E., Church, C., Hutchinson, M., Ling, J., Paradiso, S. P., Blaiszik, B. J., Foster, I. T., Gibbons, B. M., Hattrick-Simpers, J. R., et al. (2018) · Molecular Systems Design & Engineering · doi:10.1039/c8me00012c · Cited by 270 · see citing works
Brightly Blue and Green Emitting Cu(I) Dimers for Singlet Harvesting in OLEDs
Leitl, M. J., Küchle, F.-R., Mayer, H. A., Wesemann, L., & Yersin, H. (2013) · The Journal of Physical Chemistry A · doi:10.1021/jp402975d · Cited by 265 · see citing works
Towards operando computational modeling in heterogeneous catalysis
Grajciar, L., Heard, C. J., Bondarenko, A. A., Polynski, M. V., Meeprasert, J., Pidko, E. A., & Nachtigall, P. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00398j · Cited by 251 · see citing works
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A., & Kulik, H. J. (2019) · Chemical Science · doi:10.1039/c9sc02298h · Cited by 243 · see citing works
Self-citation
molSimplify: A toolkit for automating discovery in inorganic chemistry
Ioannidis, E. I., Gani, T. Z. H., & Kulik, H. J. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24437 · Cited by 235 · see citing works
Self-citation
Thermally Activated Delayed Fluorescence (TADF) and Enhancing Photoluminescence Quantum Yields of [Cu I (diimine)(diphosphine)] + Complexes—Photophysical, Structural, and Computational Studies
Linfoot, C., Leitl, M. J., Richardson, P., Rausch, A. F., Chepelin, O., White, F., Yersin, H., & Robertson, N. (2014) · Inorganic Chemistry · doi:10.1021/ic500889s · Cited by 224 · see citing works
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
Meyer, B., Sawatlon, B., Heinen, S., von Lilienfeld, O. A., & Corminboeuf, C. (2018) · Chemical Science · doi:10.1039/c8sc01949e · Cited by 209 · see citing works
Absorption Spectra of Co-ordination Compounds. I
Tsuchida, R. (1938) · Bulletin of the Chemical Society of Japan · doi:10.1246/bcsj.13.388 · Cited by 208 · see citing works
Sulfur Atoms as Ligands in Metal Complexes
Vahrenkamp, H. (1975) · Angewandte Chemie International Edition in English · doi:10.1002/anie.197503221 · Cited by 204 · see citing works
Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network
Janet, J. P., Chan, L., & Kulik, H. J. (2018) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.8b00170 · Cited by 200 · see citing works
Self-citation
Predicting electronic structure properties of transition metal complexes with neural networks
Janet, J. P., & Kulik, H. J. (2017) · Chemical Science · doi:10.1039/c7sc01247k · Cited by 199 · see citing works
Self-citation
Machine learning of molecular properties: Locality and active learning
Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5005095 · Cited by 176 · see citing works
Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry
Nandy, A., Duan, C., Janet, J. P., Gugler, S., & Kulik, H. J. (2018) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.8b04015 · Cited by 161 · see citing works
Self-citation
Toward Highly Accurate Spin State Energetics in First-Row Transition Metal Complexes: A Combined CASPT2/CC Approach
Phung, Q. M., Feldt, M., Harvey, J. N., & Pierloot, K. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00057 · Cited by 138 · see citing works
Predicting electronic structure properties of transition metal complexes with neural networks
Janet, J. P., & Kulik, H. J. (2017) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 134 · see citing works
Self-citation
Comparison of density functionals for differences between the high- (T2g5) and low- (A1g1) spin states of iron(II) compounds. IV. Results for the ferrous complexes [Fe(L)(‘NHS4’)]
Ganzenmüller, G., Berkaïne, N., Fouqueau, A., Casida, M. E., & Reiher, M. (2005) · The Journal of Chemical Physics · doi:10.1063/1.1927081 · Cited by 132 · see citing works
Absorption Spectra of Co(III) Complexes. II. Redetermination of the Spectrochemical Series
Shimura, Y., & Tsuchida, R. (1956) · Bulletin of the Chemical Society of Japan · doi:10.1246/bcsj.29.311 · Cited by 132 · see citing works
Assessment of electronic structure methods for the determination of the ground spin states of Fe( ii ), Fe( iii ) and Fe( iv ) complexes
Verma, P., Varga, Z., Klein, J. E. M. N., Cramer, C. J., QUE, L. J., & Truhlar, D. G. (2017) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp01263b · Cited by 128 · see citing works
Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network
Yao, K., Herr, J. E., Brown, S. N., & Parkhill, J. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b01072 · Cited by 126 · see citing works
Electrocatalytic Hydrogen Evolution under Acidic Aqueous Conditions and Mechanistic Studies of a Highly Stable Molecular Catalyst
Tsay, C., & Yang, J. Y. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b05851 · Cited by 124 · see citing works
Constant size descriptors for accurate machine learning models of molecular properties
Collins, C. R., Gordon, G. J., von Lilienfeld, O. A., & Yaron, D. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5020441 · Cited by 123 · see citing works
Describing Anions by Density Functional Theory: Fractional Electron Affinity
Jensen, F. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct1003324 · Cited by 122 · see citing works
The ligand field molecular mechanics model and the stereoelectronic effects of d and s electrons
Deeth, R. J. (2001) · Coordination Chemistry Reviews · doi:10.1016/s0010-8545(00)00354-4 · Cited by 120 · see citing works
Assessment of density functional theory for iron(II) molecules across the spin-crossover transition
Droghetti, A., Alfè, D., & Sanvito, S. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4752411 · Cited by 115 · see citing works
How Long Can You Make an Oxygen Chain?
McKay, D. J., & Wright, J. S. (1998) · Journal of the American Chemical Society · doi:10.1021/ja971534b · Cited by 114 · see citing works
Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry
Janet, J. P., Liu, F., Nandy, A., Duan, C., Yang, T., Lin, S., & Kulik, H. J. (2019) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.9b00109 · Cited by 114 · see citing works
Self-citation
Ironing out the photochemical and spin-crossover behavior of Fe(II) coordination compounds with computational chemistry
Ashley, D. C., & Jakubı́ková, E. (2017) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2017.02.005 · Cited by 111 · see citing works
Communication: Avoiding unbound anions in density functional calculations
Kim, M.-C., Sim, E., & Burke, K. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3590364 · Cited by 108 · see citing works
Towards quantifying the role of exact exchange in predictions of transition metal complex properties
Ioannidis, E. I., & Kulik, H. J. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4926836 · Cited by 106 · see citing works
Self-citation
Spin Propensities of Octahedral Complexes From Density Functional Theory
Mortensen, S. R., & Kepp, K. P. (2015) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5b01626 · Cited by 97 · see citing works
Catalyst design: knowledge extraction from high-throughput experimentation
Caruthers, J., Lauterbach, J., Thomson, K., Venkatasubramanian, V., Snively, C., Bhan, A., Katare, S., & Oskarsdottir, G. (2003) · Journal of Catalysis · doi:10.1016/s0021-9517(02)00036-2 · Cited by 93 · see citing works
Where Does the Density Localize in the Solid State? Divergent Behavior for Hybrids and DFT+U
Zhao, Q. X., & Kulik, H. J. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b01061 · Cited by 91 · see citing works
Self-citation
Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation
Nandy, A., Zhu, J., Janet, J. P., Duan, C., Getman, R. B., & Kulik, H. J. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b02165 · Cited by 91 · see citing works
Self-citation
Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models
Duan, C., Janet, J. P., Liu, F., Nandy, A., & Kulik, H. J. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00057 · Cited by 87 · see citing works
Self-citation
Chemical and elemental analysis of humans in vivo using improved body composition models
Heymsfield, S. B., Waki, M., Kehayias, J. J., Lichtman, S. W., Dilmanian, F. A., Kamen, Y., Wang, J., & Pierson, R. N. (1991) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1991.261.2.e190 · Cited by 81 · cited 1× · see citing works
“We enumerated candidate ligands from the elements C, N, O, P, or S, which were chosen i) for their abundance 70,71,74 on earth and in organisms and ii) to enable comparison between isovalent compounds ( i.e. , N vs.”
Multiconfiguration Pair-Density Functional Theory Predicts Spin-State Ordering in Iron Complexes with the Same Accuracy as Complete Active Space Second-Order Perturbation Theory at a Significantly Reduced Computational Cost
Wilbraham, L., Verma, P., Truhlar, D. G., Gagliardi, L., & Ciofini, I. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b00570 · Cited by 78 · see citing works
Phosphorescent Nanocluster Light‐Emitting Diodes
Kuttipillai, P. S., Zhao, Y., Traverse, C. J., Staples, R. J., Levine, B. G., & Lunt, R. R. (2015) · Advanced Materials · doi:10.1002/adma.201504548 · Cited by 76 · see citing works
Discovery of a new catalytically active and selective zeolite (ITQ-30) by high-throughput synthesis techniques
Corma, A., Díaz-Cabañas, M. J., Moliner, M., & Martı́nez, C. (2006) · Journal of Catalysis · doi:10.1016/j.jcat.2006.04.036 · Cited by 72 · see citing works
An Intelligent System for Reaction Kinetic Modeling and Catalyst Design
Katare, S. R., Caruthers, J. M., Delgass, W. N., & Venkatasubramanian, V. (2004) · Industrial & Engineering Chemistry Research · doi:10.1021/ie034067h · Cited by 72 · see citing works
Tuning the Electronic Structure of Fe(II) Polypyridines via Donor Atom and Ligand Scaffold Modifications: A Computational Study
Bowman, D. N., Bondarev, A., Mukherjee, S., & Jakubı́ková, E. (2015) · Inorganic Chemistry · doi:10.1021/acs.inorgchem.5b01409 · Cited by 71 · see citing works
DiRef, A Database of References Associated with the Spectra of Diatomic Molecules
Bernath, P. F., & McLeod, S. D. (2001) · Journal of Molecular Spectroscopy · doi:10.1006/jmsp.2001.8345 · Cited by 71 · see citing works
Transition metal methylene complexes
Herrmann, W. A., Krüger, C. R., Goddard, R., & Bernal, I. (1977) · Journal of Organometallic Chemistry · doi:10.1016/s0022-328x(00)84398-3 · Cited by 69 · see citing works
Theoretical Investigation of Paramagnetic NMR Shifts in Transition Metal Acetylacetonato Complexes: Analysis of Signs, Magnitudes, and the Role of the Covalency of Ligand–Metal Bonding
Pritchard, B., & Autschbach, J. (2012) · Inorganic Chemistry · doi:10.1021/ic300868v · Cited by 67 · see citing works
Hydrolysis of Dinuclear Ruthenium Complexes [{CpRu(PPh3)2}2(μ,η1:1‐L)][CF3SO3]2 (L=P4, P4S3): Simple Access to Metal Complexes of P2H4 and PH2SH
Barbaro, P., Vaira, M. D., Peruzzini, M., Costantini, S. S., & Stoppioni, P. (2007) · Chemistry - A European Journal · doi:10.1002/chem.200601846 · Cited by 61 · see citing works
Ligand-Field-Dependent Behavior of Meta-GGA Exchange in Transition-Metal Complex Spin-State Ordering
Ioannidis, E. I., & Kulik, H. J. (2017) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.6b11930 · Cited by 60 · see citing works
Self-citation
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Janet, J. P., Gani, T. Z. H., Steeves, A. H., Ioannidis, E. I., & Kulik, H. J. (2017) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.7b00808 · Cited by 56 · see citing works
Self-citation
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Li, Z., Omidvar, N., Chin, W. S., Robb, E., Morris, A. J., Achenie, L. E. K., & Xin, H. (2018) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.8b02842 · Cited by 54 · see citing works
Multiconfiguration Pair-Density Functional Theory for Iron Porphyrin with CAS, RAS, and DMRG Active Spaces
Zhou, C., Gagliardi, L., & Truhlar, D. G. (2019) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.8b12479 · Cited by 54 · see citing works
Ab Initio Reactive Computer Aided Molecular Design
Martı́nez, T. J. (2017) · Accounts of Chemical Research · doi:10.1021/acs.accounts.7b00010 · Cited by 51 · see citing works
Application of Semiempirical Methods to Transition Metal Complexes: Fast Results but Hard-to-Predict Accuracy
Minenkov, Y. V., Sharapa, D. I., & Cavallo, L. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00018 · Cited by 51 · see citing works
Unifying Exchange Sensitivity in Transition-Metal Spin-State Ordering and Catalysis through Bond Valence Metrics
Gani, T. Z. H., & Kulik, H. J. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00848 · Cited by 49 · see citing works
Self-citation
Computational Investigation and Design of Cobalt Aqua Complexes for Homogeneous Water Oxidation
Schilling, M., Patzke, G. R., Hutter, J., & Luber, S. (2016) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b00712 · Cited by 46 · see citing works
Scaffold Topologies. 2. Analysis of Chemical Databases
Wester, M. J., Pollock, S. N., Coutsias, E. A., Allu, T. K., Mureşan, S., & Oprea, T. I. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700342h · Cited by 44 · see citing works
Addition of methylamine to hexakis(methyl isocyanide)iron(II). Formation of an unusual chelating ligand
Miller, J. A., Balch, A. L., & Enemark, J. H. (1971) · Journal of the American Chemical Society · doi:10.1021/ja00747a058 · Cited by 43 · see citing works
Spin-state diversity in a series of Co( ii ) PNP pincer bromide complexes
Shaffer, D. W., Bhowmick, I., Rheingold, A. L., Tsay, C., Livesay, B. N., Shores, M. P., & Yang, J. Y. (2016) · Dalton Transactions · doi:10.1039/c6dt03461f · Cited by 36 · see citing works
Metal complexes of hydroxylamine
Hughes, M., & Shrimanker, K. (1976) · Inorganica Chimica Acta · doi:10.1016/s0020-1693(00)95587-7 · Cited by 17 · see citing works
Recent progress in computational exploration and design of functional materials
Luber, S. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.01.040 · Cited by 17 · see citing works

Flagged first, then by citations

Research works printed but not linked: 3

P. B. Arockiam, C. Bruneau and P. H. Dixneuf, RutheniumĲII)-Catalyzed C-H Bond Activation and Functionalization, Chem. Rev., 2012, 112, 5879-5918.
P. Bjørn Jørgensen, K. Wedel Jacobsen and M. N. Schmidt, Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials, 2018, arXiv e-prints [Online], https://ui.adsabs.harvard.edu/-abs/ 2018arXiv180603146B (accessed June 01, 2018).
H. Mimoun, Transition-Metal Peroxides-Synthesis and Use as Oxidizing Agents, in Peroxides (1983), Wiley-Blackwell, 2010, pp. 463-482.

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Authors: Gugler, S., Janet, J. P., & Kulik, H. J.
Affiliations: Massachusetts Institute of Technology (US)
Corresponding author: Kulik, Heather J.
ORCID: Gugler, Stefan
Janet, Jon Paul
Kulik, Heather J.
Venue: Molecular Systems Design & Engineering
Publisher: Royal Society of Chemistry (RSC)
Published: 2020
Volume: 5
Issue: 1
Pages: 139-152
ISSN: 2058-9689 (ISSN-L)
DOI: 10.1039/c9me00069k
OpenAlex: W2953523625
Cited by: 43
References: 116
Citations by year: 2026: 2 · 2025: 3 · 2024: 9 · 2023: 5 · 2022: 10 · 2021: 9 · 2020: 5
FWCI: 2.17
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc
creativecommons.org/licenses/by-nc/3.0/ (vor)
Locations: Molecular Systems Design & Engineering (publishedVersion, cc-by-nc)
ChemRxiv (acceptedVersion, cc-by-nc-nd)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Figshare (submittedVersion, cc-by-nc-nd)
Funders: American Association for the Advancement of Science (Marion Milligan Mason Award)
Burroughs Wellcome Fund (Career Award at the Scientific Interface)
Office of Naval Research Global (N00014-17-1-2956, N00014-18-1-2434)
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Physical Sciences › Materials Science › Electronic, Optical and Magnetic Materials › Magnetism in coordination complexes
Keywords: de novo molecular design, transition metal complexes, inorganic complexes, enumeration, spin states, machine learning training, chemical discoveries

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Enumeration of de novo inorganic complexes for chemical discovery and machine learning

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