Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Scikit-learn: Machine Learning in Python

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016

LIII. On lines and planes of closest fit to systems of points in space

Pearson, K. (1901) · The London Edinburgh and Dublin Philosophical Magazine and Journal of Science · doi:10.1080/14786440109462720 · Cited by 12,864

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

NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

Valiev, M., Bylaska, E. J., Govind, N., Kowalski, K., Straatsma, T. P., van Dam, H. J. J., Wang, D., Nieplocha, J., Aprà, E., Windus, T. L., et al. (2010) · Computer Physics Communications · doi:10.1016/j.cpc.2010.04.018 · Cited by 5,849

Computer Aided Design of Experiments

Kennard, R. W., & Stone, L. A. (1969) · Technometrics · doi:10.1080/00401706.1969.10490666 · Cited by 3,729

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

Advances in methods and algorithms in a modern quantum chemistry program package

Shao, Y., Füsti-Molnár, L., Jung, Y., Kußmann, J., Ochsenfeld, C., Brown, S. T., Gilbert, A. T. B., Slipchenko, L. V., Levchenko, S. V., O'Neill, D. P., et al. (2006) · Physical Chemistry Chemical Physics · doi:10.1039/b517914a · Cited by 2,805

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

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

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

CO2 hydrogenation to high-value products via heterogeneous catalysis

Ye, R., Ding, J., Gong, W., Argyle, M. D., Zhong, Q., Wang, Y., Russell, C. K., Xu, Z., Russell, A. G., Li, Q., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13638-9 · Cited by 1,207

DScribe: Library of descriptors for machine learning in materials science

Himanen, L., Jäger, M. O. J., Morooka, E. V., Canova, F. F., Ranawat, Y. S., Gao, D. Z., Rinke, P., & Foster, A. S. (2019) · Computer Physics Communications · doi:10.1016/j.cpc.2019.106949 · Cited by 901

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

Late-stage C–H functionalization offers new opportunities in drug discovery

Guillemard, L., Kaplaneris, N., Ackermann, L., & Johansson, M. J. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00300-6 · Cited by 845

C–H activation

Rogge, T., Kaplaneris, N., Chatani, N., Kim, J., Chang, S., Punji, B., Schafer, L. L., Musaev, D. G., Wencel‐Delord, J., Amber, C., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00041-2 · Cited by 654

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

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 371

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

The Open Reaction Database

Kearnes, S. M., Maser, M., Wleklinski, M., Kast, A., Doyle, A. G., Dreher, S. D., Hawkins, J. M., Jensen, K. F., & Coley, C. W. (2021) · Journal of the American Chemical Society · doi:10.1021/jacs.1c09820 · Cited by 355

Machine Learning in Chemical Engineering: Strengths, Weaknesses, Opportunities, and Threats

Dobbelaere, M. R., Plehiers, P. P., de Vijver, R. V., Stevens, C. V., & Geem, K. M. V. (2021) · Engineering · doi:10.1016/j.eng.2021.03.019 · Cited by 353

Holistic prediction of enantioselectivity in asymmetric catalysis

Reid, J. P., & Sigman, M. S. (2019) · Nature · doi:10.1038/s41586-019-1384-z · Cited by 322

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Gensch, T., Gomes, G., Friederich, P., Peters, E., Gaudin, T., Pollice, R., Jorner, K., Nigam, A., Lindner-D’Addario, M., Sigman, M. S., et al. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.1c09718 · Cited by 323

Adsorption Energy in Oxygen Electrocatalysis

Zhang, J., Yang, H. B., Zhou, D., & Liu, B. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.1c01003 · Cited by 314

A Structure-Based Platform for Predicting Chemical Reactivity

Sandfort, F., Strieth‐Kalthoff, F., Kühnemund, M., Beecks, C., & Glorius, F. (2020) · Chem · doi:10.1016/j.chempr.2020.02.017 · Cited by 300

Machine learning the ropes: principles, applications and directions in synthetic chemistry

Strieth‐Kalthoff, F., Sandfort, F., Segler, M., & Glorius, F. (2020) · Chemical Society Reviews · doi:10.1039/c9cs00786e · Cited by 293

Advances in asymmetric organocatalysis over the last 10 years

Xiang, S., & Tan, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17580-z · Cited by 275

Enantioselective C H Bond Functionalizations by 3d Transition-Metal Catalysts

Woźniak, Ł., & Cramer, N. (2019) · Trends in Chemistry · doi:10.1016/j.trechm.2019.03.013 · Cited by 271

Late-stage Functionalization for Improving Drug-like Molecular Properties

Castellino, N. J., Montgomery, A. P., Danon, J. J., & Kassiou, M. (2023) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00797 · Cited by 276

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Rangel-Martínez, D., Nigam, K. D. P., & Ricardez‐Sandoval, L. (2021) · Process Safety and Environmental Protection · doi:10.1016/j.cherd.2021.08.013 · Cited by 277

Autonomous Discovery in the Chemical Sciences Part II: Outlook

Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909989 · Cited by 257

Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

Strieth‐Kalthoff, F., Sandfort, F., Kühnemund, M., Schäfer, F. R., Kuchen, H., & Glorius, F. (2022) · Angewandte Chemie International Edition · doi:10.1002/anie.202204647 · Cited by 236

Quantitative Structure–Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

Zahrt, A. F., Athavale, S. V., & Denmark, S. E. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.9b00425 · Cited by 212

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Jorner, K., Brinck, T., Norrby, P., & Buttar, D. (2020) · Chemical Science · doi:10.1039/d0sc04896h · Cited by 208

Machine learning in polymer informatics

Sha, W., Li, Y., Tang, S., Tian, J., Zhao, Y., Guo, Y., Zhang, W., Zhang, X., Lu, S., Cao, Y., et al. (2021) · InfoMat · doi:10.1002/inf2.12167 · Cited by 207

Sorption-enhanced Steam Methane Reforming for Combined CO2 Capture and Hydrogen Production: A State-of-the-Art Review

Soltani, S. M., Lahiri, A., Bahzad, H., Clough, P. T., Gorbounov, M., & Yan, Y. (2021) · Carbon Capture Science & Technology · doi:10.1016/j.ccst.2021.100003 · Cited by 213

AI Applications through the Whole Life Cycle of Material Discovery

Li, J., Lim, K., Yang, H., Ren, Z., Raghavan, S., Chen, P., Buonassisi, T., & Wang, X. (2020) · Matter · doi:10.1016/j.matt.2020.06.011 · Cited by 198

Automation and computer-assisted planning for chemical synthesis

Shen, Y., Borowski, J. E., Hardy, M. A., Sarpong, R., Doyle, A. G., & Cernak, T. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00022-5 · Cited by 204

Design of functional binders for high-specific-energy lithium-ion batteries: from molecular structure to electrode properties

Tian, Q., Yang, H., Li, Q., Yu, X., & Li, H. (2023) · Industrial Chemistry and Materials · doi:10.1039/d3im00089c · Cited by 192

Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

Galan, E. A., Zhao, H., Wang, X., Dai, Q., Huck, W. T. S., & Ma, S. (2020) · Matter · doi:10.1016/j.matt.2020.08.034 · Cited by 194

Automated in Silico Design of Homogeneous Catalysts

Foscato, M., & Jensen, V. R. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04952 · Cited by 190

Inverse design workflow discovers hole-transport materials tailored for perovskite solar cells

Wu, J., Torresi, L., Hu, M., Reiser, P., Zhang, J., Rocha‐Ortiz, J. S., Wang, L., Xie, Z., Zhang, K., Park, B., et al. (2024) · Science · doi:10.1126/science.ads0901 · Cited by 196

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 193

Evaluation guidelines for machine learning tools in the chemical sciences

Bender, A., Schneider, N., Segler, M., Walters, W. P., Engkvist, O., & Rodrigues, T. (2022) · Nature Reviews Chemistry · doi:10.1038/s41570-022-00391-9 · Cited by 196

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

Unleashing the Power of Artificial Intelligence in Materials Design

Badini, S., Regondi, S., & Pugliese, R. (2023) · Materials · doi:10.3390/ma16175927 · Cited by 193

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 186

Organic reactivity from mechanism to machine learning

Jorner, K., Tomberg, A., Bauer, C. A., Sköld, C., & Norrby, P. (2021) · Nature Reviews Chemistry · doi:10.1038/s41570-021-00260-x · Cited by 191

Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors

Guan, Y., Coley, C. W., Wu, H., Ranasinghe, D. S., Heid, E., Struble, T. J., Pattanaik, L., Green, W. H., & Jensen, K. F. (2020) · Chemical Science · doi:10.1039/d0sc04823b · Cited by 175

Accelerated dinuclear palladium catalyst identification through unsupervised machine learning

Hueffel, J. A., Sperger, T., Funes‐Ardoiz, I., Ward, J. S., Rissanen, K. T., & Schoenebeck, F. (2021) · Science · doi:10.1126/science.abj0999 · Cited by 167

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

Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis

Moon, J., Beker, W., Siek, M., Kim, J., Lee, H. S., Hyeon, T., & Grzybowski, B. A. (2023) · Nature Materials · doi:10.1038/s41563-023-01707-w · Cited by 171

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Zhou, J., & Huang, M. (2024) · Chemical Society Reviews · doi:10.1039/d4cs00196f · Cited by 168

Artificial Intelligence to Power the Future of Materials Science and Engineering

Sha, W., Guo, Y., Yuan, Q. M., Tang, S., Zhang, X., Lu, S., Guo, X., Cao, Y., & Cheng, S. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.201900143 · Cited by 166

Toward Machine Learning-Enhanced High-Throughput Experimentation

Eyke, N. S., Koscher, B. A., & Jensen, K. F. (2021) · Trends in Chemistry · doi:10.1016/j.trechm.2020.12.001 · Cited by 156

Recent Developments and Trends in Asymmetric Organocatalysis

Mancheño, O. G., & Waser, M. (2022) · European Journal of Organic Chemistry · doi:10.1002/ejoc.202200950 · Cited by 145

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction

Heid, E., & Green, W. H. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00975 · Cited by 144

A machine-learning tool to predict substrate-adaptive conditions for Pd-catalyzed C–N couplings

Rinehart, N. I., Saunthwal, R. K., Wellauer, J., Zahrt, A. F., Schlemper, L., Shved, A. S., Bigler, R., Fantasia, S., & Denmark, S. E. (2023) · Science · doi:10.1126/science.adg2114 · Cited by 149

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 151

tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes

Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation

Singh, S., Pareek, M., Changotra, A., Banerjee, S., Bhaskararao, B., Balamurugan, P., & Sunoj, R. B. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1916392117 · Cited by 134

Predicting Regioselectivity in Radical C−H Functionalization of Heterocycles through Machine Learning

Li, X., Zhang, S., Xu, L., & Hong, X. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202000959 · Cited by 137

% V Bur index and steric maps: from predictive catalysis to machine learning

Escayola, S., Bahri‐Laleh, N., & Poater, A. (2023) · Chemical Society Reviews · doi:10.1039/d3cs00725a · Cited by 135

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 142

The digitization of organic synthesis

Davies, I. W. (2019) · Nature · doi:10.1038/s41586-019-1288-y · Cited by 127

A map of the amine–carboxylic acid coupling system

Mahjour, B. A., Shen, Y., Liu, W., & Cernak, T. (2020) · Nature · doi:10.1038/s41586-020-2142-y · Cited by 126

Interfacing single-atom catalysis with continuous-flow organic electrosynthesis

Bajada, M. A., Sanjosé‐Orduna, J., Liberto, G. D., Tosoni, S., Pacchioni, G., Noël, T., & Vilé, G. (2022) · Chemical Society Reviews · doi:10.1039/d2cs00100d · Cited by 123

Artificial intelligence for performance prediction of organic solvent nanofiltration membranes

Hu, J., Kim, C., Halasz, P., Kim, J. F., Kim, J., & Székely, G. (2020) · Journal of Membrane Science · doi:10.1016/j.memsci.2020.118513 · Cited by 123

A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

Bhowmik, A., Castelli, I. E., García-Lastra, J. M., Jørgensen, P. B., Winther, O., & Vegge, T. (2019) · Energy storage materials · doi:10.1016/j.ensm.2019.06.011 · Cited by 122

Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

Xie, Y., Sattari, K., Zhang, C., & Lin, J. (2022) · Progress in Materials Science · doi:10.1016/j.pmatsci.2022.101043 · Cited by 129

Embracing data science in catalysis research

Suvarna, M., & Pérez‐Ramírez, J. (2024) · Nature Catalysis · doi:10.1038/s41929-024-01150-3 · Cited by 137

An automatic end-to-end chemical synthesis development platform powered by large language models

Ruan, Y., Lu, C., Xu, N., He, Y., Chen, Y.-X., Zhang, J., Xuan, J., Pan, J., Fang, Q., Gao, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54457-x · Cited by 163

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 120

Molecular Representation: Going Long on Fingerprints

Pattanaik, L., & Coley, C. W. (2020) · Chem · doi:10.1016/j.chempr.2020.05.002 · Cited by 117

Combining machine learning and high-throughput experimentation to discover photocatalytically active organic molecules

Li, X., Maffettone, P. M., Che, Y., Liu, T., Chen, L., & Cooper, A. I. (2021) · Chemical Science · doi:10.1039/d1sc02150h · Cited by 115

Characterizing Uncertainty in Machine Learning for Chemistry

Heid, E., McGill, C. J., Vermeire, F. H., & Green, W. H. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00373 · Cited by 128

A review on the application of molecular descriptors and machine learning in polymer design

Zhao, Y., Mulder, R. J., Houshyar, S., & Le, T. C. (2023) · Polymer Chemistry · doi:10.1039/d3py00395g · Cited by 118

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 107

Automatic strain sensor design via active learning and data augmentation for soft machines

Yang, H., Li, J., Lim, K. Z., Pan, C., Truong, T. V., Wang, Q., Li, K., Li, S., Xiao, X., Ding, M., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-021-00434-8 · Cited by 105

Machine Learning for Chemistry: Basics and Applications

Shi, Y., Yang, Z., Ma, S., Kang, P., Shang, C., Hu, P., & Liu, Z. (2023) · Engineering · doi:10.1016/j.eng.2023.04.013 · Cited by 113

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Nandy, A., Duan, C., & Kulik, H. J. (2021) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2021.100778 · Cited by 113

Dataset Design for Building Models of Chemical Reactivity

Raghavan, P., Haas, B. C., Ruos, M. E., Schleinitz, J., Doyle, A. G., Reisman, S. E., Sigman, M. S., & Coley, C. W. (2023) · ACS Central Science · doi:10.1021/acscentsci.3c01163 · Cited by 105

Machine learning integrated photocatalysis: progress and challenges

Ge, L., Ke, Y., & Li, X. (2023) · Chemical Communications · doi:10.1039/d3cc00989k · Cited by 98

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 101

From gray to blue hydrogen: Trends and forecasts of catalysts and sorbents for unit process

Ahn, S.-Y., Kim, K., Kim, B., Hong, G.-R., Jang, W.-J., Bae, J. W., Park, Y.-K., Jeon, B., & Roh, H. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113635 · Cited by 94

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Henle, J. J., Zahrt, A. F., Rose, B. T., Darrow, W. T., Wang, Y., & Denmark, S. E. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c04715 · Cited by 93

Multilabel Classification Models for the Prediction of Cross-Coupling Reaction Conditions

Maser, M., Cui, A. Y., Ryou, S., DeLano, T. J., Yue, Y., & Reisman, S. E. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01234 · Cited by 97

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

Artificial intelligence and automation in computer aided synthesis planning

Thakkar, A., Johansson, S., Jorner, K., Buttar, D., Reymond, J., & Engkvist, O. (2020) · Reaction Chemistry & Engineering · doi:10.1039/d0re00340a · Cited by 93

Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges

Singh, S., & Sunoj, R. B. (2023) · Accounts of Chemical Research · doi:10.1021/acs.accounts.2c00801 · Cited by 89

Controlling Stereoselectivity with Noncovalent Interactions in Chiral Phosphoric Acid Organocatalysis

Betinol, I. O., Kuang, Y., Mulley, B. P., & Reid, J. P. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00869 · Cited by 88

Multimetallic-Catalyzed C–C Bond-Forming Reactions: From Serendipity to Strategy

Ackerman, L. K. G., Kariofillis, S. K., & Weix, D. J. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c08615 · Cited by 84

Nitrogen-Based Organofluorine Functional Molecules: Synthesis and Applications

Liu, S., Zhou, J., Yu, L., Liu, Y., Huang, Y., Ouyang, Y., Liu, G., Xu, X., & Shibata, N. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00661 · Cited by 88

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

Elevating Chemistry Research with a Modern Electronics Toolkit

Prabhu, G. R. D., & Urban, P. L. (2020) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00206 · Cited by 85

Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning

Xu, L., Zhang, S., Li, X., Tang, M., Xie, P., & Hong, X. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202106880 · Cited by 83

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Wang, Y., Xie, T., Tang, Q., Wang, M., Ying, T., Zhu, H., & Zeng, X. (2022) · Journal of Magnesium and Alloys · doi:10.1016/j.jma.2021.12.007 · Cited by 82

Enantioselectivity prediction of pallada-electrocatalysed C–H activation using transition state knowledge in machine learning

Xu, L., Frey, J., Hou, X., Zhang, S., Li, Y., Oliveira, J. C. A., Li, S.-W., Ackermann, L., & Hong, X. (2023) · Nature Synthesis · doi:10.1038/s44160-022-00233-y · Cited by 83

Computer-aided understanding and engineering of enzymatic selectivity

Wu, L., Qin, L., Nie, Y., Xu, Y., & Zhao, Y. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107793 · Cited by 79

Predicting reaction conditions from limited data through active transfer learning

Shim, E., Kammeraad, J. A., Xu, Z., Tewari, A., Cernak, T., & Zimmerman, P. M. (2022) · Chemical Science · doi:10.1039/d1sc06932b · Cited by 80

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Li, S., Wu, H., Menon, A., Spiekermann, K., Li, Y., & Green, W. H. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c04670 · Cited by 81

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Janet, J. P., & Kulik, H. J. (2020) · ACS in focus · doi:10.1021/acs.infocus.7e4001 · Cited by 81

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Liao, M., Lan, K., & Yao, Y. (2021) · Journal of Industrial Ecology · doi:10.1111/jiec.13214 · Cited by 84

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Zahrt, A. F., Mo, Y., Nandiwale, K. Y., Shprints, R., Heid, E., & Jensen, K. F. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c08997 · Cited by 75

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Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Liu, M., Lin, Y., Zhao, X., Yan, K., et al. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000115 · Cited by 214

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Yeston, J. (2019) · Science · doi:10.1126/science.363.6424.241-m

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