Machine learning meets volcano plots: computational discovery of cross-coupling catalysts

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

A consistent and accurate <i>ab initio</i> parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu

Grimme, S., Antony, J., Ehrlich, S., & Krieg, H. (2010) · The Journal of Chemical Physics · doi:10.1063/1.3382344 · Cited by 59,686 · see citing works

Support-vector networks

Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works

The Nature of Statistical Learning Theory

Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works

Support-Vector Networks

Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565 · see citing works

Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy

Weigend, F., & Ahlrichs, R. (2005) · Physical Chemistry Chemical Physics · doi:10.1039/b508541a · Cited by 30,761 · see citing works

Effect of the damping function in dispersion corrected density functional theory

Grimme, S., Ehrlich, S., & Goerigk, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21759 · Cited by 28,221 · 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

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Lu, Z. Q. J. (2010) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2010.00646_6.x · Cited by 12,904 · see citing works

Palladium-Catalyzed Cross-Coupling Reactions of Organoboron Compounds

Miyaura, N., & Suzuki, A. (1995) · Chemical Reviews · doi:10.1021/cr00039a007 · Cited by 12,878 · see citing works

Combining theory and experiment in electrocatalysis: Insights into materials design

Seh, Z. W., Kibsgaard, J., Dickens, C. F., Chorkendorff, I., Nørskov, J. K., & Jaramillo, T. F. (2017) · Science · doi:10.1126/science.aad4998 · Cited by 12,114 · 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

Self-Consistent Molecular-Orbital Methods. IX. An Extended Gaussian-Type Basis for Molecular-Orbital Studies of Organic Molecules

Ditchfield, R., Hehre, W. J., & Pople, J. A. (1971) · The Journal of Chemical Physics · doi:10.1063/1.1674902 · Cited by 10,612 · see citing works

Stochastic gradient boosting

Friedman, J. H. (2002) · Computational Statistics &amp; Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works

Trends in the exchange current for hydrogen evolution

Nørskov, J. K., Bligaard, T., Logadottir, A., Kitchin, J. R., Chen, J. G., Pandelov, S., & Stimming, U. (2005) · Journal of The Electrochemical Society · doi:10.1149/1.1856988 · Cited by 6,121 · see citing works

Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94

Halgren, T. A. (1996) · Journal of Computational Chemistry · doi:10.1002/(sici)1096-987x(199604)17:5/6 · Cited by 5,321 · 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

Self-consistent molecular orbital methods. 21. Small split-valence basis sets for first-row elements

Binkley, J. S., Pople, J. A., & Hehre, W. J. (1980) · Journal of the American Chemical Society · doi:10.1021/ja00523a008 · Cited by 3,891 · see citing works

N-Heterocyclic Carbenes: A New Concept in Organometallic Catalysis

Herrmann, W. A. (2002) · Angewandte Chemie International Edition · doi:10.1002/1521-3773(20020415)41:8 · Cited by 3,764 · see citing works

Palladium-Catalyzed Suzuki−Miyaura Cross-Coupling Reactions Employing Dialkylbiaryl Phosphine Ligands

Martı́n, R., & Buchwald, S. L. (2008) · Accounts of Chemical Research · doi:10.1021/ar800036s · Cited by 2,651 · 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

Recent advances in homogeneous nickel catalysis

Tasker, S. Z., Standley, E. A., & Jamison, T. F. (2014) · Nature · doi:10.1038/nature13274 · Cited by 2,301 · 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

A new stereospecific cross-coupling by the palladium-catalyzed reaction of 1-alkenylboranes with 1-alkenyl or 1-alkynyl halides

Miyaura, N., Yamada, K., & Suzuki, A. K. (1979) · Tetrahedron Letters · doi:10.1016/s0040-4039(01)95429-2 · Cited by 2,241 · see citing works

Self-consistent molecular-orbital methods. 22. Small split-valence basis sets for second-row elements

Gordon, M. S., Binkley, J. S., Pople, J. A., Pietro, W. J., & Hehre, W. J. (1982) · Journal of the American Chemical Society · doi:10.1021/ja00374a017 · Cited by 2,104 · see citing works

Cross‐Coupling Reactions Of Organoboranes: An Easy Way To Construct CC Bonds (Nobel Lecture)

Suzuki, A. (2011) · Angewandte Chemie International Edition · doi:10.1002/anie.201101379 · Cited by 2,054 · see citing works

Biaryl Phosphane Ligands in Palladium‐Catalyzed Amination

Surry, D. S., & Buchwald, S. L. (2008) · Angewandte Chemie International Edition · doi:10.1002/anie.200800497 · Cited by 2,012 · 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

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

The Mechanism of Water Oxidation: From Electrolysis via Homogeneous to Biological Catalysis

Dau, H., Limberg, C., Reier, T., Risch, M., Roggan, S., & Strasser, P. (2010) · ChemCatChem · doi:10.1002/cctc.201000126 · Cited by 1,783 · 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

Inertia and driving force of chemical reactions

Evans, M. G., & Polanyi, M. F. D. (1938) · Transactions of the Faraday Society · doi:10.1039/tf9383400011 · Cited by 1,714 · see citing works

Interaction of the van der Waals Type Between Three Atoms

Axilrod, B. M., & Teller, E. (1943) · The Journal of Chemical Physics · doi:10.1063/1.1723844 · Cited by 1,685 · see citing works

SMILES. 2. Algorithm for generation of unique SMILES notation

Weininger, D., Weininger, A., & Weininger, J. L. (1989) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00062a008 · Cited by 1,286 · see citing works

Self-consistent molecular orbital methods. 24. Supplemented small split-valence basis sets for second-row elements

Pietro, W. J., Francl, M. M., Hehre, W. J., DeFrees, D. J., Pople, J. A., & Binkley, J. S. (1982) · Journal of the American Chemical Society · doi:10.1021/ja00383a007 · Cited by 1,174 · see citing works

Transition-metal-catalyzed Suzuki–Miyaura cross-coupling reactions: a remarkable advance from palladium to nickel catalysts

Han, F. (2013) · Chemical Society Reviews · doi:10.1039/c3cs35521g · Cited by 1,129 · see citing works

Electrocatalysis in the anodic evolution of oxygen and chlorine

Trasatti, S. P. M. (1984) · Electrochimica Acta · doi:10.1016/0013-4686(84)85004-5 · Cited by 1,068 · see citing works

The rate of electrolytic hydrogen evolution and the heat of adsorption of hydrogen

Parsons, R. (1958) · Transactions of the Faraday Society · doi:10.1039/tf9585401053 · Cited by 1,039 · see citing works

The Electrocatalysis of Oxygen Evolution on Perovskites

Bockris, J. O., & Otagawa, T. (1984) · Journal of The Electrochemical Society · doi:10.1149/1.2115565 · Cited by 958 · 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

Schiff Base Catalysts for the Asymmetric Strecker Reaction Identified and Optimized from Parallel Synthetic Libraries

Sigman, M. S., & Jacobsen, E. N. (1998) · Journal of the American Chemical Society · doi:10.1021/ja980139y · Cited by 910 · see citing works

Some Relations between Reaction Rates and Equilibrium Constants.

Hammett, L. P. (1935) · Chemical Reviews · doi:10.1021/cr60056a010 · Cited by 871 · see citing works

Advances in Neural Information Processing Systems 7

Tesauro, G., Touretzky, D. S., & Leen, T. K. (1995) · Cited by 869 · see citing works

Introducing structural sensitivity into adsorption–energy scaling relations by means of coordination numbers

Calle‐Vallejo, F., Loffreda, D., Koper, M. T. M., & Sautet, P. (2015) · Nature Chemistry · doi:10.1038/nchem.2226 · Cited by 834 · 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

Machine learning unifies the modeling of materials and molecules

Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances · doi:10.1126/sciadv.1701816 · Cited by 764 · see citing works

Metal‐Catalyzed Cross‐Coupling Reactions and More

Meijere, A. D., Bräse, S. 1., & Oestreich, M. (2013) · Wiley eBooks · doi:10.1002/9783527655588 · Cited by 716 · see citing works

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

Machine learning of molecular electronic properties in chemical compound space

Montavon, G., Rupp, M., Gobre, V., Vazquez-Mayagoitia, A., Hansen, K., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2013) · New Journal of Physics · doi:10.1088/1367-2630/15/9/095003 · Cited by 631 · see citing works

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400195d · Cited by 615 · see citing works

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

Ulissi, Z. W., Medford, A. J., Bligaard, T., & Nørskov, J. K. (2017) · Nature Communications · doi:10.1038/ncomms14621 · Cited by 614 · see citing works

Nanomole-scale high-throughput chemistry for the synthesis of complex molecules

Santanilla, A. B., Regalado, E. L., Pereira, T., Shevlin, M., Bateman, K. P., Campeau, L., Schneeweis, J. E., Berritt, S., Shi, Z., Nantermet, P. G., et al. (2014) · Science · doi:10.1126/science.1259203 · Cited by 606 · 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

The theory of reactions involving proton transfers

Bell, R. P. (1936) · Proceedings of the Royal Society of London A Mathematical and Physical Sciences · doi:10.1098/rspa.1936.0060 · Cited by 579 · see citing works

Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties

Le, T. C., Epa, V. C., Burden, F. R., & Winkler, D. A. (2012) · Chemical Reviews · doi:10.1021/cr200066h · Cited by 563 · see citing works

Computational Studies of Synthetically Relevant Homogeneous Organometallic Catalysis Involving Ni, Pd, Ir, and Rh: An Overview of Commonly Employed DFT Methods and Mechanistic Insights

Sperger, T., Sanhueza, I. A., Kalvet, I., & Schoenebeck, F. (2015) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00163 · Cited by 552 · 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

Crystal structure representations for machine learning models of formation energies

Faber, F. A., Lindmaa, A., von Lilienfeld, O. A., & Armiento, R. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24917 · Cited by 457 · 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

Machine learning of molecular electronic properties in chemical compound space

Montavon, G., Rupp, M., Gobre, V. V., Vázquez‐Mayagoitia, Á., Hansen, K., Tkatchenko, A., Müller, K., & von Lilienfeld, O. A. (2013) · MPG.PuRe (Max Planck Society) · doi:10.3929/ethz-b-000071765 · Cited by 414 · see citing works

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO ₂ Reduction

Ulissi, Z. W., Tang, M. T., Xiao, J., Liu, X., Torelli, D. A., Karamad, M., Cummins, K., Hahn, C., Lewis, N. S., Jaramillo, T. F., et al. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b01648 · Cited by 387 · see citing works

Cobalt Precursors for High-Throughput Discovery of Base Metal Asymmetric Alkene Hydrogenation Catalysts

Friedfeld, M. R., Shevlin, M., Hoyt, J. M., Krska, S. W., Tudge, M. T., & Chirik, P. J. (2013) · Science · doi:10.1126/science.1243550 · Cited by 382 · see citing works

Alchemical and structural distribution based representation for universal quantum machine learning

Faber, F. A., Christensen, A. S., Huang, B., & von Lilienfeld, O. A. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5020710 · Cited by 360 · see citing works

High-throughput screening of bimetallic catalysts enabled by machine learning

Li, Z., Wang, S., Chin, W. S., Achenie, L. E. K., & Xin, H. (2017) · Journal of Materials Chemistry A · doi:10.1039/c7ta01812f · Cited by 359 · see citing works

The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond

Sigman, M. S., Harper, K. C., Bess, E. N., & Milo, A. (2016) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6b00194 · Cited by 357 · see citing works

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

Timoshenko, J., Lu, D., Lin, Y., & Frenkel, A. I. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b02364 · Cited by 306 · see citing works

Die katalytische Zersetzung des Nitramids und ihre physikalisch-chemische Bedeutung

Brönsted, J. N., & Pedersen, K. J. (1924) · Zeitschrift für Physikalische Chemie · doi:10.1515/zpch-1924-10814 · Cited by 305 · see citing works

Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture

Fernández, M., Boyd, P. G., Daff, T. D., Aghaji, M. Z., & Woo, T. K. (2014) · The Journal of Physical Chemistry Letters · doi:10.1021/jz501331m · Cited by 297 · see citing works

Palladium catalyzed asymmetric Suzuki–Miyaura coupling reactions to axially chiral biaryl compounds: Chiral ligands and recent advances

Zhang, D., & Wang, Q. (2014) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2014.11.011 · Cited by 296 · 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

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

A Simple, Multidimensional Approach to High-Throughput Discovery of Catalytic Reactions

Robbins, D., & Hartwig, J. F. (2011) · Science · doi:10.1126/science.1207922 · Cited by 278 · see citing works

The road from animal electricity to green energy: combining experiment and theory in electrocatalysis

Greeley, J., & Marković, N. M. (2012) · Energy & Environmental Science · doi:10.1039/c2ee21754f · Cited by 272 · see citing works

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

Jinnouchi, R., & Asahi, R. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b02010 · Cited by 260 · see citing works

Quantum Machine Learning in Chemical Compound Space

von Lilienfeld, O. A. (2017) · Angewandte Chemie International Edition · doi:10.1002/anie.201709686 · Cited by 240 · see citing works

Contemporary screening approaches to reaction discovery and development

Collins, K. D., Gensch, T., & Glorius, F. (2014) · Nature Chemistry · doi:10.1038/nchem.2062 · Cited by 238 · see citing works

“New Trends in Cross-Coupling: Theory and Applications”

Snieckus, R. B. V. (2016) · Johnson Matthey Technology Review · doi:10.1595/205651316x690925 · Cited by 236 · see citing works

High-Throughput and Parallel Screening Methods in Asymmetric Hydrogenation

Jakel, C., & Paciello, R. (2006) · Chemical Reviews · doi:10.1021/cr040675a · Cited by 227 · see citing works

Parameterization of phosphine ligands reveals mechanistic pathways and predicts reaction outcomes

Niemeyer, Z. L., Milo, A., Hickey, D. P., & Sigman, M. S. (2016) · Nature Chemistry · doi:10.1038/nchem.2501 · Cited by 221 · see citing works

Three-Dimensional Correlation of Steric and Electronic Free Energy Relationships Guides Asymmetric Propargylation

Harper, K. C., & Sigman, M. S. (2011) · Science · doi:10.1126/science.1206997 · Cited by 214 · see citing works

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b01456 · Cited by 211 · see citing works

Chemical Pressure-Driven Enhancement of the Hydrogen Evolving Activity of Ni2P from Nonmetal Surface Doping Interpreted via Machine Learning

Wexler, R. B., Martirez, J. M. P., & Rappe, A. M. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b00947 · Cited by 209 · see citing works

Linear free energy relationships in rate and equilibrium phenomena

Hammett, L. P. (1938) · Transactions of the Faraday Society · doi:10.1039/tf9383400156 · Cited by 207 · see citing works

Copper-catalysed cross-coupling: an untapped potential

Thapa, S., Shrestha, B. L., Gurung, S. K., & Giri, R. (2015) · Organic & Biomolecular Chemistry · doi:10.1039/c5ob00200a · Cited by 200 · 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

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

Mechanismus der Elektrolytischen Wasserstoffabscheidung und Adsorptionsenergie von Atomarem Wasserstoff

Gerischer, H. (1958) · Bulletin des Sociétés Chimiques Belges · doi:10.1002/bscb.19580670714 · Cited by 174 · see citing works

Machine-learning prediction of the d-band center for metals and bimetals

Takigawa, I., Shimizu, K., Tsuda, K., & Takakusagi, S. (2016) · RSC Advances · doi:10.1039/c6ra04345c · Cited by 174 · see citing works

Theoretical Insight into the C−C Coupling Reactions of the Vinyl, Phenyl, Ethynyl, and Methyl Complexes of Palladium and Platinum

Ananikov, V. P., Musaev, D. G., & Morokuma, K. (2005) · Organometallics · doi:10.1021/om0490841 · Cited by 172 · see citing works

Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity

Fernandez, M., Trefiak, N. R., & Woo, T. K. (2013) · The Journal of Physical Chemistry C · doi:10.1021/jp404287t · Cited by 167 · see citing works

Predictive Structure–Reactivity Models for Rapid Screening of Pt-Based Multimetallic Electrocatalysts for the Oxygen Reduction Reaction

Xin, H., Holewinski, A., & Linic, S. (2011) · ACS Catalysis · doi:10.1021/cs200462f · Cited by 150 · see citing works

Cluster-Bildung in Cu—Fe-Mischkristallen und Bestimmung der Löslichkeit von Eisen in Kupfer im Temperaturbereich von 450—1000°C

Nagorny, K., & Hein, H. (1977) · Zeitschrift für Physikalische Chemie · doi:10.1524/zpch.1977.108.2.185 · Cited by 145 · 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

Learning Curves: Asymptotic Values and Rate of Convergence

Cortes, C., Jackel, L. D., Solla, S. A., Vapnik, V. N., & Denker, J. S. (1993) · Cited by 128 · see citing works

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

Bereau, T., Andrienko, D., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00301 · Cited by 125 · see citing works

Linear scaling relationships and volcano plots in homogeneous catalysis – revisiting the Suzuki reaction

Busch, M., Wodrich, M. D., & Corminbœuf, C. (2015) · Chemical Science · doi:10.1039/c5sc02910d · Cited by 124 · see citing works

Metal-catalyzed cross-coupling reactions and more

de Meijere, A., Bräse, S., & Oestreich, M. (2014) · Wiley-VCH eBooks · Cited by 123 · see citing works

Active learning with non- ab initio input features toward efficient CO 2 reduction catalysts

Noh, J., Back, S., Kim, J., & Jung, Y. (2018) · Chemical Science · doi:10.1039/c7sc03422a · Cited by 118 · see citing works

Applied Cross-Coupling Reactions

Nishihara, Y. (2012) · Lecture notes in chemistry · doi:10.1007/978-3-642-32368-3 · Cited by 117 · see citing works

Pd-PEPPSI: Pd-NHC Precatalyst for Suzuki–Miyaura Cross-Coupling Reactions of Amides

Lei, P., Meng, G., Ling, Y., An, J., & Szostak, M. (2017) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.7b00749 · Cited by 114 · see citing works

Palladium-Catalyzed Enantioselective Redox-Relay Heck Arylation of 1,1-Disubstituted Homoallylic Alcohols

Chen, Z., Hilton, M. J., & Sigman, M. S. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b06994 · Cited by 111 · see citing works

Predictive modeling in homogeneous catalysis: a tutorial

Maldonado, A. G., & Rothenberg, G. (2010) · Chemical Society Reviews · doi:10.1039/b921393g · Cited by 110 · see citing works

Developing a Modern Approach To Account for Steric Effects in Hammett-Type Correlations

Santiago, C. B., Milo, A., & Sigman, M. S. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b08799 · Cited by 103 · see citing works

Automated Discovery and Construction of Surface Phase Diagrams Using Machine Learning

Ulissi, Z. W., Singh, A. R., Tsai, C., & Nørskov, J. K. (2016) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6b01254 · Cited by 102 · see citing works

Developing Comprehensive Computational Parameter Sets To Describe the Performance of Pyridine-Oxazoline and Related Ligands

Guo, J.-Y., Minko, Y., Santiago, C. B., & Sigman, M. S. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b00739 · Cited by 89 · see citing works

A Generalized Picture of C–C Cross-Coupling

Busch, M., Wodrich, M. D., & Corminbœuf, C. (2017) · ACS Catalysis · doi:10.1021/acscatal.7b01415 · Cited by 87 · see citing works

Accessing and predicting the kinetic profiles of homogeneous catalysts from volcano plots

Wodrich, M. D., Busch, M., & Corminbœuf, C. (2016) · Chemical Science · doi:10.1039/c6sc01660j · Cited by 85 · see citing works

Genetic Optimization of Training Sets for Improved Machine Learning Models of Molecular Properties

Browning, N. J., Ramakrishnan, R., von Lilienfeld, O. A., & Röthlisberger, U. (2017) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.7b00038 · Cited by 79 · see citing works

Heterogeneous catalyst discovery using 21st century tools: a tutorial

Ras, E., & Rothenberg, G. (2014) · RSC Advances · doi:10.1039/c3ra45852k · Cited by 78 · see citing works

Rationale for the sluggish oxidative addition of aryl halides to Au( i )

Livendahl, M., Goehry, C., Maseras, F., & Echavarren, A. M. (2013) · Chemical Communications · doi:10.1039/c3cc48914k · Cited by 75 · see citing works

Adsorption of CO on Low-Energy, Low-Symmetry Pt Nanoparticles: Energy Decomposition Analysis and Prediction via Machine-Learning Models

Gasper, R. J., Shi, H., & Ramasubramaniam, A. (2017) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.6b12800 · Cited by 73 · see citing works

Emergence of Copper‐Mediated Formation of C–C Bonds

Maaliki, C., Thiery, E., & Thibonnet, J. (2016) · European Journal of Organic Chemistry · doi:10.1002/ejoc.201600540 · Cited by 69 · see citing works

Platinum Catalysts for Suzuki Biaryl Coupling Reactions

Bedford, R. B., Hazelwood, S. L., & Albisson, D. A. (2002) · Organometallics · doi:10.1021/om0202524 · Cited by 63 · see citing works

Intramolecular Transmetalation of Arylpalladium(II) and Arylplatinum(II) Complexes with Silanes and Stannanes

Mateo, C., Fernández-Rivas, C., Cárdenas, D. J., & Echavarren, A. M. (1998) · Organometallics · doi:10.1021/om980157o · Cited by 51 · see citing works

Machine-learning models for combinatorial catalyst discovery

Landrum, G. A., Penzotti, J. E., & Putta, S. (2005) · Measurement Science and Technology · doi:10.1088/0957-0233/16/1/035 · Cited by 48 · see citing works

Mechanical Catalysis

(2008) · Wiley eBooks · doi:10.1002/9780470384190 · Cited by 45 · see citing works

Density Functional Study of Mo-Carbonyl-Catalyzed Alkynol Cycloisomerization: Comparison with W-Catalyzed Reaction

Nowroozi-Isfahani, T., Musaev, D. G., McDonald, F. E., & Morokuma, K. (2005) · Organometallics · doi:10.1021/om050255r · Cited by 43 · see citing works

A machine learning approach to graph-theoretical cluster expansions of the energy of adsorbate layers

Vignola, E., Steinmann, S. N., Vandegehuchte, B. D., Curulla‐Ferré, D., Stamatakis, M., & Sautet, P. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4985890 · Cited by 42 · see citing works

Predicting the performance of oxidation catalysts using descriptor models

Madaan, N., Shiju, N. R., & Rothenberg, G. (2015) · Catalysis Science & Technology · doi:10.1039/c5cy00932d · Cited by 40 · see citing works

Copper-Catalyzed C(sp3)–OH Cleavage with Concomitant C–C Coupling: Synthesis of 3-Substituted Isoindolinones

Rao, H. S. P., & Rao, A. V. B. (2015) · The Journal of Organic Chemistry · doi:10.1021/jo502446k · Cited by 39 · see citing works

On the Generality of Molecular Volcano Plots

Wodrich, M. D., Sawatlon, B., Busch, M., & Corminbœuf, C. (2017) · ChemCatChem · doi:10.1002/cctc.201701709 · Cited by 35 · see citing works

Group Additivity and Modified Linear Scaling Relations for Estimating Surface Thermochemistry on Transition Metal Surfaces: Application to Furanics

Vorotnikov, V., & Vlachos, D. G. (2015) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.5b01696 · Cited by 33 · see citing works

Understanding Organometallic Reaction Mechanisms and Catalysis

(2014) · Wiley eBooks · doi:10.1002/9783527678211 · Cited by 32 · see citing works

Construction of Terminal Conjugated Enynes: Cu-Mediated Cross-Coupling Reaction of Alkenyldialkylborane with (Trimethylsilyl)ethynyl Bromide

Hoshi, M., Kawamura, N., & Shirakawa, K. (2006) · Synthesis · doi:10.1055/s-2006-942373 · Cited by 30 · see citing works

Improving the Thermodynamic Profiles of Prospective Suzuki–Miyaura Cross‐Coupling Catalysts by Altering the Electrophilic Coupling Component

Busch, M., Wodrich, M. D., & Corminbœuf, C. (2017) · ChemCatChem · doi:10.1002/cctc.201701710 · Cited by 24 · see citing works

Scaling relationship for oscillating electrochemical systems: dependence of phase diagram on electrode size and rotation rate

Kiss, I. Z., Kazsu, Z., & Gáspár, V. (2009) · Physical Chemistry Chemical Physics · doi:10.1039/b905295j · Cited by 21 · see citing works

Cross Coupling and Heck-Type Reactions 1

Molander, G. A., Biscoe, M. R., Carboni, B., Carreaux, F., Chang, W.-T. T., Chartoire, A., Colobert, F., Corpet, M., Denmark, S., Echavarren, A. M., et al. (2013) · Thieme eBooks · doi:10.1055/b-003-125729 · Cited by 21 · see citing works

New tricks by very old dogs: predicting the catalytic hydrogenation of HMF derivatives using Slater-type orbitals

Ras, E., Louwerse, M. J., & Rothenberg, G. (2012) · Catalysis Science & Technology · doi:10.1039/c2cy20193c · Cited by 18 · see citing works

Mechanical Catalysis: Methods of Enzymatic, Homogeneous, and Heterogeneous Catalysis

Swiegers, G. F. (2008) · Cited by 14 · see citing works

qmlcode/qml: Release v0.3.1

Christensen, A. S., Faber, F. A., Huang, B., et al. (2017) · Figshare · doi:10.5281/zenodo.817332 · Cited by 13 · see citing works

Understanding Organometallic Reaction Mechanisms and Catalysis: Computational and Experimental Tools

Ananikov, V. P. (2014) · Cited by 10 · see citing works

Quantum machine learning using atom-in-molecule-based fragments selected on-the-flyPreprint

Huang, B., & von Lilienfeld, O. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.04146 · Cited by 9 · see citing works

Untitled work

(2001) · doi:10.1002/1521-3773(20010119)40:2

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849 · see citing works

Machine Learning for Catalysis Informatics: Recent Applications and Prospects

Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617 · see citing works

A Critical Review of Machine Learning of Energy Materials

Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609 · see citing works

Using nature’s blueprint to expand catalysis with Earth-abundant metals

Bullock, R. M., Chen, J. G., Gagliardi, L., Chirik, P. J., Farha, O. K., Hendon, C. H., Jones, C. W., Keith, J. A., Klosin, J., Minteer, S. D., et al. (2020) · Science · doi:10.1126/science.abc3183 · Cited by 603 · see citing works

Machine-learned potentials for next-generation matter simulations

Friederich, P., Häse, F., Proppe, J., & Aspuru‐Guzik, A. (2021) · Nature Materials · doi:10.1038/s41563-020-0777-6 · Cited by 558 · see citing works

Quantum Chemistry in the Age of Machine Learning

Dral, P. O. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b03664 · Cited by 422 · see citing works

Exploring chemical compound space with quantum-based machine learning

von Lilienfeld, O. A., Müller, K., & Tkatchenko, A. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0189-9 · Cited by 376 · 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

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · 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

Machine Learning in Catalysis, From Proposal to Practicing

Yang, W., Fidelis, T. T., & Sun, W. (2019) · ACS Omega · doi:10.1021/acsomega.9b03673 · Cited by 226 · see citing works

Machine learning for advanced energy materials

Yun, L., Esan, O. C., Pan, Z., & An, L. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100049 · Cited by 215 · see citing works

Perspective on integrating machine learning into computational chemistry and materials science

Westermayr, J., Gastegger, M., Schütt, K. T., & Maurer, R. J. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0047760 · Cited by 209 · see citing works

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

Freeze, J. G., Kelly, H. R., & Batista, V. S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00759 · Cited by 207 · see citing works

Ab Initio Machine Learning in Chemical Compound Space

Huang, B., & von Lilienfeld, O. A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01303 · Cited by 201 · see citing works

Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization

Janet, J. P., Ramesh, S., Duan, C., & Kulik, H. J. (2020) · ACS Central Science · doi:10.1021/acscentsci.0c00026 · Cited by 200 · see citing works

Gold Catalyzed Cross-Coupling Reactions: An Overview of Design Strategies, Mechanistic Studies, and Applications.

Nijamudheen, A., & Datta, A. (2019) · Chemistry · doi:10.1002/chem.201903377 · Cited by 129 · see citing works

Automated in Silico Design of Homogeneous Catalysts

Foscato, M., & Jensen, V. R. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04952 · Cited by 186 · see citing works

The Evolution of Data-Driven Modeling in Organic Chemistry

Williams, W. L., Zeng, L., Gensch, T., Sigman, M. S., Doyle, A. G., & Anslyn, E. V. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00535 · Cited by 186 · see citing works

Roadmap on Machine learning in electronic structure

Kulik, H. J., Hammerschmidt, T., Schmidt, J., Botti, S., Marques, M. A. L., Boley, M., Scheffler, M., Todorović, M., Rinke, P., Oses, C., et al. (2022) · Electronic Structure · doi:10.1088/2516-1075/ac572f · Cited by 183 · see citing works

High-throughput experimentation meets artificial intelligence: a new pathway to catalyst discovery

McCullough, K., Williams, T., Mingle, K., Jamshidi, P., & Lauterbach, J. A. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp00972e · Cited by 180 · see citing works

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

Friederich, P., dos Passos Gomes, G., De Bin, R., Aspuru-Guzik, A., & Balcells, D. (2020) · Chemical Science · doi:10.1039/d0sc00445f · Cited by 179 · see citing works

Machine-Learning-Guided Discovery and Optimization of Additives in Preparing Cu Catalysts for CO2 Reduction

Guo, Y., He, X., Su, Y., Dai, Y., Xie, M., Yang, S., Chen, J., Wang, K., Zhou, D., & Wang, C. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c00339 · Cited by 164 · see citing works

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

Tu, Z., Stuyver, T., & Coley, C. W. (2022) · Chemical Science · doi:10.1039/d2sc05089g · Cited by 141 · see citing works

Recent advances in knowledge discovery for heterogeneous catalysis using machine learning

Günay, M. E., & Yıldırım, R. (2020) · Catalysis Reviews · doi:10.1080/01614940.2020.1770402 · Cited by 136 · see citing works

Machine intelligence for chemical reaction space

Schwaller, P., Vaucher, A. C., Laplaza, R., Bunne, C., Krause, A., Corminbœuf, C., & Laino, T. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1604 · Cited by 131 · see citing works

Representation of molecular structures with persistent homology for machine learning applications in chemistry

Townsend, J., Micucci, C. P., Hymel, J. H., Maroulas, V., & Vogiatzis, K. D. (2020) · Nature Communications · doi:10.1038/s41467-020-17035-5 · Cited by 123 · see citing works

Beyond Density Functional Theory: The Multiconfigurational Approach To Model Heterogeneous Catalysis

Gaggioli, C. A., Stoneburner, S. J., Cramer, C. J., & Gagliardi, L. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b01775 · Cited by 122 · see citing works

The Genesis of Molecular Volcano Plots

Wodrich, M. D., Sawatlon, B., Busch, M., & Corminbœuf, C. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00857 · Cited by 122 · see citing works

Applications of Machine Learning in Alloy Catalysts: Rational Selection and Future Development of Descriptors

Yang, Z. K., & Gao, W. (2022) · Advanced Science · doi:10.1002/advs.202106043 · Cited by 122 · see citing works

Machine learning for design principles for single atom catalysts towards electrochemical reactions

Tamtaji, M., Gao, H., Hossain, M. D., Galligan, P. R., Wong, H., Liu, Z., Liu, H., Cai, Y., Goddard, W. A., & Luo, Z. (2022) · Journal of Materials Chemistry A · doi:10.1039/d2ta02039d · Cited by 119 · see citing works

Deep dive into machine learning density functional theory for materials science and chemistry

Fiedler, L., Shah, K., Bußmann, M., & Cangi, A. (2022) · Physical Review Materials · doi:10.1103/physrevmaterials.6.040301 · Cited by 117 · see citing works

Designing promising molecules for organic solar cells via machine learning assisted virtual screening

Sahu, H., Yang, F., Ye, X., Ma, J., Fang, W., & Ma, H. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta04097h · Cited by 116 · see citing works

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization

Stuke, A., Rinke, P., & Todorović, M. (2021) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abee59 · Cited by 109 · see citing works

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts

Gallarati, S., Fabregat, R., Laplaza, R., Bhattacharjee, S., Wodrich, M. D., & Corminbœuf, C. (2021) · Chemical Science · doi:10.1039/d1sc00482d · Cited by 104 · see citing works

Artificial Intelligence to Accelerate the Discovery of N 2 Electroreduction Catalysts

Kim, M., Yeo, B. C., Park, Y., Lee, H. M., Han, S. S., & Kim, D.-H. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b03686 · Cited by 101 · see citing works

Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability, and explainability

Stuyver, T., & Coley, C. W. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0079574 · Cited by 98 · see citing works

Atomic structures and orbital energies of 61,489 crystal-forming organic molecules

Stuke, A., Künkel, C., Golze, D., Todorović, M., Margraf, J. T., Reuter, K., Rinke, P., & Oberhofer, H. (2020) · Scientific Data · doi:10.1038/s41597-020-0385-y · Cited by 96 · see citing works

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

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

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Gomes, G., Pollice, R., & Aspuru‐Guzik, A. (2021) · Trends in Chemistry · doi:10.1016/j.trechm.2020.12.006 · Cited by 87 · see citing works

Chemical diversity in molecular orbital energy predictions with kernel ridge regression

Stuke, A., Todorović, M., Rupp, M., Künkel, C., Ghosh, K., Himanen, L., & Rinke, P. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5086105 · Cited by 87 · see citing works

Electrocatalytic Oxygen Reduction to Produce Hydrogen Peroxide: Rational Design from Single-Atom Catalysts to Devices

Tong, Y., Wang, L., Hou, F., Dou, S. X., & Liang, J. (2022) · Electrochemical Energy Reviews · doi:10.1007/s41918-022-00163-5 · Cited by 85 · see citing works

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Aldossary, A., Campos-Gonzalez-Angulo, J. A., Pablo‐García, S., Leong, S. X., Rajaonson, E. M., Thiede, L., Tom, G., Wang, A., Avagliano, D., & Aspuru‐Guzik, A. (2024) · Advanced Materials · doi:10.1002/adma.202402369 · Cited by 84 · see citing works

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

Maley, S. M., Kwon, D.-H., Rollins, N., Stanley, J. C., Sydora, O. L., Bischof, S. M., & Ess, D. H. (2020) · Chemical Science · doi:10.1039/d0sc03552a · Cited by 81 · see citing works

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions

Taylor, M. G., Yang, T., Lin, S., Nandy, A., Janet, J. P., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.0c01458 · Cited by 81 · see citing works

Machine Learning in Chemistry

Janet, J. P., & Kulik, H. J. (2020) · ACS in focus · doi:10.1021/acs.infocus.7e4001 · Cited by 77 · see citing works

In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling

Saxena, S., Khan, T. S., Jalid, F., Ramteke, M., & Haider, M. A. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta07651d · Cited by 75 · see citing works

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Nandy, A., Duan, C., Goffinet, C., & Kulik, H. J. (2022) · JACS Au · doi:10.1021/jacsau.2c00176 · Cited by 74 · see citing works

Activity-Based Screening of Homogeneous Catalysts through the Rapid Assessment of Theoretically Derived Turnover Frequencies

Wodrich, M. D., Sawatlon, B., Solel, E., Kozuch, S., & Corminbœuf, C. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b00717 · Cited by 71 · 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

Data-Driven Advancement of Homogeneous Nickel Catalyst Activity for Aryl Ether Cleavage

Cordova, M., Wodrich, M. D., Meyer, B., Sawatlon, B., & Corminboeuf, C. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00774 · Cited by 68 · see citing works

Computational catalysis for metal-organic frameworks: An overview

McCarver, G. A., Rajeshkumar, T., & Vogiatzis, K. D. (2021) · Coordination Chemistry Reviews · doi:10.1016/j.ccr.2021.213777 · Cited by 68 · see citing works

Catalyze Materials Science with Machine Learning

Kim, J., Kang, D., Kim, S., & Jang, H. W. (2021) · ACS Materials Letters · doi:10.1021/acsmaterialslett.1c00204 · Cited by 67 · see citing works

Autonomous 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

A reactivity model for oxidative addition to palladium enables quantitative predictions for catalytic cross-coupling reactions

Lu, J., Donnecke, S., Paci, I., & Leitch, D. C. (2022) · Chemical Science · doi:10.1039/d2sc00174h · Cited by 65 · see citing works

Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling

Gensch, T., Smith, S. R., Colacot, T. J., Timsina, Y. N., Xu, G., Glasspoole, B. W., & Sigman, M. S. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c01970 · Cited by 64 · see citing works

Recent progress on discovery and properties prediction of energy materials: Simple machine learning meets complex quantum chemistry

Kang, Y., Li, L., & Li, B. (2020) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2020.05.044 · Cited by 63 · 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

Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design

Künkel, C., Schober, C., Margraf, J. T., Reuter, K., & Oberhofer, H. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.8b04436 · Cited by 59 · see citing works

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Liu, F., Duan, C., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02288 · Cited by 58 · see citing works

When machine learning meets molecular synthesis

Oliveira, J. C. A., Frey, J., Zhang, S., Xu, L., Li, X., Li, S., Hong, X., & Ackermann, L. (2022) · Trends in Chemistry · doi:10.1016/j.trechm.2022.07.005 · Cited by 58 · see citing works

Catalysis: energy is the measure of all things

Solel, E., Tarannam, N., & Kozuch, S. (2019) · Chemical Communications · doi:10.1039/c9cc00754g · Cited by 57 · see citing works

Putting Density Functional Theory to the Test in Machine-Learning-Accelerated Materials Discovery

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c00631 · Cited by 55 · see citing works

Neural network potentials for accelerated metadynamics of oxygen reduction kinetics at Au–water interfaces

Yang, X. L., Bhowmik, A., Vegge, T., & Hansen, H. A. (2023) · Chemical Science · doi:10.1039/d2sc06696c · Cited by 55 · see citing works

Data-Driven Acceleration of the Coupled-Cluster Singles and Doubles Iterative Solver

Townsend, J., & Vogiatzis, K. D. (2019) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b01442 · Cited by 53 · see citing works

Emerging Landscape of Computational Modeling in Pharmaceutical Development

Abramov, Y. A., Sun, G., & Zeng, Q. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01580 · Cited by 52 · see citing works

Surface effect of the MgCl2 support in Ziegler–Natta catalyst for ethylene polymerization: A computational study

Xie, K., Xu, S., Hao, W., Wang, J., Huang, A., & Zhang, Y. (2022) · Applied Surface Science · doi:10.1016/j.apsusc.2022.153002 · Cited by 51 · see citing works

AI Approaches to Homogeneous Catalysis with Transition Metal Complexes

Morán‐González, L., Burnage, A. L., Nova, A., & Balcells, D. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c01202 · Cited by 51 · see citing works

Moving Frontiers in Transition Metal Catalysis: Synthesis, Characterization and Modeling

Sharapa, D. I., Doronkin, D. E., Studt, F., Grunwaldt, J., & Behrens, S. S. (2019) · Advanced Materials · doi:10.1002/adma.201807381 · Cited by 51 · see citing works

Designing catalysts with deep generative models and computational data. A case study for Suzuki cross coupling reactions

Schilter, O. T., Vaucher, A. C., Schwaller, P., & Laino, T. (2023) · Digital Discovery · doi:10.1039/d2dd00125j · Cited by 50 · see citing works

Scaling Relationships and Volcano Plots in Homogeneous Catalysis

Anand, M., Rohr, B. A., Statt, M. J., & Nørskov, J. K. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c01991 · Cited by 50 · see citing works

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Nandy, A., & Kulik, H. J. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c04300 · Cited by 50 · see citing works

Applications of Quantum Chemistry in Pharmaceutical Process Development: Current State and Opportunities

Lam, Y., Abramov, Y. A., Ananthula, R. S., Elward, J. M., Hilden, L. R., Lill, S. O. N., Norrby, P., Ramı́rez, A., Sherer, E. C., Mustakis, J. G., et al. (2020) · Organic Process Research & Development · doi:10.1021/acs.oprd.0c00222 · Cited by 48 · see citing works

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Kulik, H. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1439 · Cited by 47 · see citing works

Data-Driven Approaches Can Overcome the Cost–Accuracy Trade-Off in Multireference Diagnostics

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00358 · Cited by 47 · see citing works

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Laplaza, R., Gallarati, S., & Corminbœuf, C. (2022) · Chemistry - Methods · doi:10.1002/cmtd.202100107 · Cited by 47 · see citing works

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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

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

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Laplaza, R., Das, S., Wodrich, M. D., & Corminbœuf, C. (2022) · Nature Protocols · doi:10.1038/s41596-022-00726-2 · Cited by 44 · see citing works

Enumeration of <i>de novo</i> inorganic complexes for chemical discovery and machine learning

Gugler, S., Janet, J. P., & Kulik, H. J. (2020) · Molecular Systems Design &amp; Engineering · doi:10.1039/c9me00069k · Cited by 43 · see citing works

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Heinen, S., Schwilk, M., von Rudorff, G. F., & von Lilienfeld, O. A. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab6ac4 · Cited by 43 · see citing works

Reaction mechanisms at the homogeneous–heterogeneous frontier: insights from first-principles studies on ligand-decorated metal nanoparticles

Ortuño, M. Á., & López, N. (2019) · Catalysis Science & Technology · doi:10.1039/c9cy01351b · Cited by 43 · see citing works

Grand Challenges in Computational Catalysis

Studt, F. (2021) · Frontiers in Catalysis · doi:10.3389/fctls.2021.658965 · Cited by 42 · see citing works

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Farrar, E. H. E., & Grayson, M. N. (2022) · Chemical Science · doi:10.1039/d2sc02925a · Cited by 41 · see citing works

Computational Evolution Of New Catalysts For The Morita–Baylis–Hillman Reaction**

Seumer, J., Hansen, J. K. S., Nielsen, M. B., & Jensen, J. H. (2023) · Angewandte Chemie International Edition · doi:10.1002/anie.202218565 · Cited by 41 · see citing works

Unraveling Metal/Pincer Ligand Effects in the Catalytic Hydrogenation of Carbon Dioxide to Formate

Sawatlon, B., Wodrich, M. D., & Corminbœuf, C. (2018) · Organometallics · doi:10.1021/acs.organomet.8b00490 · Cited by 40 · see citing works

Machine Learning to Predict Diels–Alder Reaction Barriers from the Reactant State Electron Density

Vargas, S., Hennefarth, M. R., Liu, Z. J., & Alexandrova, A. N. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00623 · Cited by 40 · see citing works

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Fey, N., & Lynam, J. M. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1590 · Cited by 38 · see citing works

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Kneiding, H., & Balcells, D. (2024) · Chemical Science · doi:10.1039/d4sc02934h · Cited by 37 · see citing works

Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning

Lumiaro, E., Todorović, M., Kurtén, T., Vehkamäki, H., & Rinke, P. (2021) · Atmospheric chemistry and physics · doi:10.5194/acp-21-13227-2021 · Cited by 37 · see citing works

Design of an Accurate Machine Learning Algorithm to Predict the Binding Energies of Several Adsorbates on Multiple Sites of Metal Surfaces

Praveen, C. S., & Comas‐Vives, A. (2020) · ChemCatChem · doi:10.1002/cctc.202000517 · Cited by 36 · see citing works

Data-powered augmented volcano plots for homogeneous catalysis

Wodrich, M. D., Fabrizio, A., Meyer, B., & Corminbœuf, C. (2020) · Chemical Science · doi:10.1039/d0sc04289g · Cited by 36 · see citing works

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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

An In‐Depth Mechanistic Study of Ru‐Catalysed Aqueous Methanol Dehydrogenation and Prospects for Future Catalyst Design

Govindarajan, N., Sinha, V., Trincado, M., Grützmacher, H., Meijer, E. J., & de Bruin, B. (2020) · ChemCatChem · doi:10.1002/cctc.202000057 · Cited by 35 · see citing works

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Duan, C., Liu, F., Nandy, A., & Kulik, H. J. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.0c02018 · Cited by 35 · see citing works

Machine learning-based analysis of overall stability constants of metal–ligand complexes

Kanahashi, K., Urushihara, M., & Yamaguchi, K. (2022) · Scientific Reports · doi:10.1038/s41598-022-15300-9 · Cited by 34 · see citing works

Accurate and rapid prediction of p K a of transition metal complexes: semiempirical quantum chemistry with a data-augmented approach

Sinha, V., Laan, J. J., & Pidko, E. A. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp05281g · Cited by 34 · see citing works

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Zhang, Y., Xiong, Y., Wang, Y., Wang, Q., & Fan, J. (2025) · Nanoscale Horizons · doi:10.1039/d5nh00216h · Cited by 33 · see citing works

Transferable MP2-Based Machine Learning for Accurate Coupled-Cluster Energies

Townsend, J., & Vogiatzis, K. D. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00927 · Cited by 32 · see citing works

Molecular Volcano Plots: Tools for Rationalizing and Predicting the Performance of Homogeneous Catalysts

Sawatlon, B. (2021) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-8639

Statistical Analysis and Discovery of Heterogeneous Catalysts Based on Machine Learning from Diverse Published Data

Suzuki, K., Toyao, T., Maeno, Z., Takakusagi, S., Shimizu, K., & Takigawa, I. (2019) · ChemCatChem · doi:10.1002/cctc.201900971 · Cited by 91 · see citing works

Computation and Machine Learning for Catalyst Discovery

Mok, D. H., Lee, W., Kim, J., Jung, H. D., Jang, H. Y., Moon, S., Lee, C., & Back, S. (2022) · Ceramist · doi:10.31613/ceramist.2022.25.2.08 · Cited by 2 · see citing works

Machine Learning-Aided Identification of Single Atom Alloy Catalysts

Dasgupta, A., Gao, Y., Broderick, S., Pitman, E. B., & Rajan, K. (2020) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.0c01492 · Cited by 41 · see citing works

Design of Experimental Conditions with Machine Learning for Collaborative Organic Synthesis Reactions Using Transition-Metal Catalysts

Ebi, T., Sen, A., Dhital, R. N., Yamada, Y. M. A., & Kaneko, H. (2021) · ACS Omega · doi:10.1021/acsomega.1c04826 · Cited by 21 · see citing works