Interactive-quantum-chemical-descriptors enabling accurate prediction of an activation energy through machine learning

Machine learning for molecular and materials science

Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works

Quantum-Chemical Descriptors in QSAR/QSPR Studies

Karelson, M. M., Lobanov, V. S., & KATRITZKY, A. R. (1996) · Chemical Reviews · doi:10.1021/cr950202r · Cited by 1,627 · see citing works

Towards the online computer-aided design of catalytic pockets

Falivene, L., Cao, Z., Petta, A., Serra, L., Poater, A., Oliva, R., Scarano, V., & Cavallo, L. (2019) · Nature Chemistry · doi:10.1038/s41557-019-0319-5 · Cited by 1,290 · see citing works

Percent buried volume for phosphine and N-heterocyclic carbene ligands: steric properties in organometallic chemistry

Clavier, H., & Nolan, S. P. (2010) · Chemical Communications · doi:10.1039/b922984a · Cited by 1,100 · see citing works

A robotic platform for flow synthesis of organic compounds informed by AI planning

Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V. L., Hart, T., Fishman, J., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,063 · see citing works

Predicting reaction performance in C–N cross-coupling using machine learning

Ahneman, D. T., Estrada, J. G., Lin, S., Dreher, S. D., & Doyle, A. G. (2018) · Science · doi:10.1126/science.aar5169 · Cited by 1,058 · see citing works

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,045 · see citing works

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 · see citing works

Machine Learning Directed Search for Ultraincompressible, Superhard Materials

Tehrani, A. M., Oliynyk, A. O., Parry, M. E., Rizvi, Z., Couper, S., Lin, F., Miyagi, L., Sparks, T. D., & Brgoch, J. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b02717 · Cited by 318 · see citing works

Natural Energy Decomposition Analysis: Extension to Density Functional Methods and Analysis of Cooperative Effects in Water Clusters

Glendening, E. D. (2005) · The Journal of Physical Chemistry A · doi:10.1021/jp058209s · Cited by 301 · see citing works

Deoxyfluorination with Sulfonyl Fluorides: Navigating Reaction Space with Machine Learning

Nielsen, M. K., Ahneman, D. T., Riera, O., & Doyle, A. G. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b01523 · Cited by 259 · see citing works

Natural Energy Decomposition Analysis: Explicit Evaluation of Electrostatic and Polarization Effects with Application to Aqueous Clusters of Alkali Metal Cations and Neutrals

Glendening, E. D. (1996) · Journal of the American Chemical Society · doi:10.1021/ja951834y · Cited by 224 · see citing works

Correlating Reactivity and Selectivity to Cyclopentadienyl Ligand Properties in Rh(III)-Catalyzed C–H Activation Reactions: An Experimental and Computational Study

Piou, T., Romanov‐Michailidis, F., Romanova‐Michaelides, M., Jackson, K. E., Semakul, N., Taggart, T. D., Newell, B., Rithner, C. D., Paton, R. S., & Rovis, T. (2017) · Journal of the American Chemical Society · doi:10.1021/jacs.6b11670 · Cited by 193 · see citing works

Quantification of the Steric Influence of Alkylphosphine–Sulfonate Ligands on Polymerization, Leading to High-Molecular-Weight Copolymers of Ethylene and Polar Monomers

Yusuke, O., Ito, S., Kuroda, J.-I., Okumura, Y., & Nozaki, K. (2014) · Journal of the American Chemical Society · doi:10.1021/ja505558e · Cited by 162 · see citing works

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies

Sumita, M., Yang, X., Ishihara, S., Tamura, R., & Tsuda, K. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00213 · Cited by 134 · see citing works

AI-Assisted Exploration of Superionic Glass-Type Li + Conductors with Aromatic Structures

Hatakeyama‐Sato, K., Tezuka, T., Umeki, M., & Oyaizu, K. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.9b11442 · Cited by 111 · see citing works

A Model to Search for Synthesizable MoleculesPreprint

Bradshaw, J., Paige, B., Kusner, M. J., Segler, M., & Hernández-Lobato, J. M. (2019) · arXiv (Cornell University) · doi:10.5555/3454287.3455000 · Cited by 77 · see citing works

Aryne Polymerization Enabling Straightforward Synthesis of Elusive Poly(ortho-arylene)s

Mizukoshi, Y., Mikami, K., & Uchiyama, M. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5112207 · Cited by 72 · see citing works

Machine Learning Approach for Prediction of Reaction Yield with Simulated Catalyst Parameters

Yada, A., Nagata, K., Ando, Y., Matsumura, T., Ichinoseki, S., & Sato, K. (2018) · Chemistry Letters · doi:10.1246/cl.171130 · Cited by 67 · see citing works

Catalytic Regio- and Enantioselective Proton Migration from Skipped Enynes to Allenes

Wei, X., Wakaki, T., Itoh, T., Li, H., Yoshimura, T., Miyazaki, A., Oisaki, K., Hatanaka, M., Shimizu, Y., & Kanai, M. (2019) · Chem · doi:10.1016/j.chempr.2018.11.022 · Cited by 61 · see citing works

DFT and QSAR Studies of Ethylene Polymerization by Zirconocene Catalysts

Parveen, R., Cundari, T. R., Younker, J. M., Rodríguez, G., & McCullough, L. G. (2019) · ACS Catalysis · doi:10.1021/acscatal.9b02925 · Cited by 51 · see citing works

QSAR study of N -substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods

Chtita, S., Aouidate, A., Belhassan, A., Ousaa, A., Taourati, A. I., Elidrissi, B., Ghamali, M., Bouachrıne, M., & Lakhlifi, T. (2019) · New Journal of Chemistry · doi:10.1039/c9nj04909f · Cited by 41 · see citing works

Prediction of Transition-State Energies of Hydrodeoxygenation Reactions on Transition-Metal Surfaces Based on Machine Learning

Abdelfatah, K. E., Yang, W., Solomon, R. V., Rajbanshi, B., Chowdhury, A., Zare, M., Kundu, S. K., Yonge, A., Heyden, A., & Terejanu, G. (2019) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.9b10507 · Cited by 31 · see citing works

Elucidating Enzymatic Catalysis Using Fast Quantum Chemical Descriptors

Grillo, I. B., Urquiza‐Carvalho, G. A., Bachega, J. F. R., & Rocha, G. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00860 · Cited by 25 · see citing works

Development of a quantum chemical descriptor expressing aromatic/quinoidal character for designing narrow-bandgap π-conjugated polymers

Hayashi, Y., & Kawauchi, S. (2019) · Polymer Chemistry · doi:10.1039/c9py00987f · Cited by 21 · see citing works

Placing Metal in the Bowl: Does Rim Alkylation Matter?

Rogachev, A. Y., Liu, S., Xu, Q., Li, J., Zhou, Z. Y., Spisak, S. N., Wei, Z., & Petrukhina, M. A. (2019) · Organometallics · doi:10.1021/acs.organomet.8b00837 · Cited by 12 · see citing works

Origin of stereoselectivity and multidimensional quantitative analysis of ligand effects on yttrium-catalysed polymerization of 2-vinylpyridine

Zhao, Y., Han, L., Luo, G., Kang, X., Hou, Z., & Luo, Y. (2019) · Catalysis Science & Technology · doi:10.1039/c9cy01670h · Cited by 11 · see citing works

Quantum-chemically-calculated mechanistically interpretable molecular descriptors for drug-action mechanism study – a case study of anthracycline anticancer antibiotics

Pang, S.-K. (2016) · RSC Advances · doi:10.1039/c6ra14630a · Cited by 8 · see citing works

Exploring energetics of dirhodium paddlewheel complexes with π-ligands of different topologies

Rogachev, A. Y., Li, J., & Petrukhina, M. A. (2017) · Journal of Organometallic Chemistry · doi:10.1016/j.jorganchem.2017.07.013 · Cited by 2 · 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

% 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 131 · see citing works

Machine learning activation energies of chemical reactions

Lewis‐Atwell, T., Townsend, P. A., & Grayson, M. N. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1593 · Cited by 85 · 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

Graph-Driven Reaction Discovery: Progress, Challenges, and Future Opportunities

Ismail, I., Majerus, R. C., & Habershon, S. (2022) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.2c06408 · Cited by 50 · see citing works

Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction

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

Application of machine learning to reveal relationship between processing-structure-property for polypropylene injection molding

Wu, F.-Y., Yin, J., Chen, S.-C., Gao, X., Zhou, L., Lü, Y., Lei, J., Zhong, G., & Li, Z. (2023) · Polymer · doi:10.1016/j.polymer.2023.125736 · Cited by 40 · see citing works

Machine learning in polymer science: A new lens for physical and chemical exploration

Cao, X.-Q., Zhang, Y., Sun, Z., Yin, H., & Feng, Y. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101544 · Cited by 37 · see citing works

A machine learning method to predict rate constants for various reactions in combustion kinetic models

Li, N., Girhe, S. D., Zhang, M., Chen, B., Zhang, Y., Liu, S., & Pitsch, H. G. (2024) · Combustion and Flame · doi:10.1016/j.combustflame.2024.113375 · Cited by 30 · see citing works

Hydrogen Evolution Prediction for Alternating Conjugated Copolymers Enabled by Machine Learning with Multidimension Fragmentation Descriptors

Xu, Y., Ju, C., Li, B., Ma, Q.-S., Chen, Z., Zhang, L., & Chen, J. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c05536 · Cited by 27 · see citing works

Graph to Activation Energy Models Easily Reach Irreducible Errors but Show Limited Transferability

Vadaddi, S. M., Zhao, Q., & Savoie, B. M. (2024) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c07240 · Cited by 22 · see citing works

Representations and strategies for transferable machine learning improve model performance in chemical discovery

Harper, D. R., Nandy, A., Arunachalam, N., Duan, C., Janet, J. P., & Kulik, H. J. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0082964 · Cited by 22 · see citing works

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information

Marques, E. A., Gendt, S. D., Pourtois, G., & van Setten, M. J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01502 · Cited by 11 · see citing works

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning

Taylor, M. G., Nandy, A., Lu, C. C., & Kulik, H. J. (2021) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.1c02852 · Cited by 11 · see citing works

Machine‐Learning‐Based Design of Metallocene Catalysts for Controlled Olefin Copolymerization

Kim, Y., Kim, Y., Kim, H., Kang, S., Kim, J., Lee, K.-H., Jeong, W., Lee, W. J., Ryu, H., Kim, K., et al. (2025) · Chemistry - A European Journal · doi:10.1002/chem.202500316 · Cited by 5 · see citing works

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Kirkland, J. K., Kumawat, J., Tameh, M. S., Tolman, T., Lambert, A., Lief, G. R., Yang, Q., & Ess, D. H. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01575 · Cited by 5 · see citing works

Molecular field analysis in half-titanocene complexes: computational study towards data-driven in silico optimization of single-site olefin polymerization catalysts

Yamaguchi, S., Kikuchi, T., Tanaka, K., & Takamiya, I. (2024) · Catalysis Science & Technology · doi:10.1039/d4cy00241e · Cited by 5 · see citing works

Prediction of Activation Energies of Organic Molecules With at Most Seven Non‐Hydrogen Atoms Using Quantum‐Chemically Assisted ML

Kalamatianos, K., & Flenga, O. N. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70083 · Cited by 3 · see citing works

Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability

Ghorai, S., Staub, R., Harabuchi, Y., Nakano, T., Varnek, A. A., & Maeda, S. (2026) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c02120 · Cited by 4 · see citing works

Advancements in Machine Learning Predicting Activation and Gibbs Free Energies in Chemical Reactions

Cao, G. (2025) · International Journal of Quantum Chemistry · doi:10.1002/qua.70036 · Cited by 2 · see citing works

Multimodal descriptor integration and transfer learning for rapid yet accurate prediction metallocene ethylene-insertion activity

yang, T., Wu, Q., Liang, J., Qin, Y., Wei, X., Wang, Y., Wen, D., Jia, P., Zhu, Y., Zhao, S., et al. (2026) · Journal of Catalysis · doi:10.1016/j.jcat.2026.116824 · Cited by 2 · see citing works

Design of metallocene catalysts for enhanced ethylene/1-octene copolymerization performance via machine learning and DFT

Lu, C.-J., Zhu, L., Huang, J., Jiang, J., Ruan, S., Zhou, Y., & Luo, Z.-H. (2026) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2026.07.028

Development of Reaction Prediction Scheme Based on Machine Learning with Quantum Chemical Descriptors

Fujinami, M., Seino, J., & Nakai, H. (2016) · Journal of Computer Chemistry Japan · doi:10.2477/jccj.2016-0040

Quantum Chemical Reaction Prediction Method Based on Machine Learning

Fujinami, M., Seino, J., & Nakai, H. (2020) · Bulletin of the Chemical Society of Japan · doi:10.1246/bcsj.20200017 · Cited by 24 · see citing works

Prediction of Activation Energies of Organic Molecules With at Most Seven Non‐Hydrogen Atoms Using Quantum‐Chemically Assisted ML

Kalamatianos, K., & Flenga, O. N. (2025) · Journal of Computational Chemistry · doi:10.1002/jcc.70083 · Cited by 3 · see citing works

A Quantum Lens on Molecular Design: A Machine-Learned Energy Function from Interacting Quantum AtomsPreprint

Hoffmann, M., Kazimir, A., Oestereich, T., Kaermer, L., Engelberger, F., Meiler, J., & Lamers, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.03.709242

Unlocking the predictive power of quantum-inspired representations for intermolecular properties in machine learning

Santiago, R., Vela, S., Deumal, M., & Ribas‐Ariño, J. (2023) · Digital Discovery · doi:10.1039/d3dd00187c · Cited by 3 · see citing works