Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

Puck van Gerwen, Ksenia R. Briling, Yannick Calvino Alonso, Malte Franke and Clemence Corminboeuf

Digital Discovery, 2024, 3, 932
DOI: 10.1039/D3DD00175J

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

Sílvia Escayola, Naeimeh Bahri-Laleh and Albert Poater

Chem. Soc. Rev., 2024, 53, 853
DOI: 10.1039/D3CS00725A

Mechanism-informed machine learning for rational catalyst design: application to regioselectivity of allyl acetate hydroformylation

Minhua Zhang, Yuzhe Sun, Yifei Chen, Lingtao Wang, Kai Song and Hao Gong

Catal. Sci. Technol., 2026, 16, 944
DOI: 10.1039/D5CY01398D

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Jeremy J. Henle and Scott E. Denmark

React. Chem. Eng., 2021, 6, 694
DOI: 10.1039/D1RE00013F

Deep learning metal complex properties with natural quantum graphs

Hannes Kneiding, Ruslan Lukin, Lucas Lang, Simen Reine, Thomas Bondo Pedersen, Riccardo De Bin and David Balcells

Digital Discovery, 2023, 2, 618
DOI: 10.1039/D2DD00129B

Low-cost machine learning prediction of excited state properties of iridium-centered phosphors

Gianmarco G. Terrones, Chenru Duan, Aditya Nandy and Heather J. Kulik

Chem. Sci., 2023, 14, 1419
DOI: 10.1039/D2SC06150C

Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening

Cheng-Han Li and Daniel P. Tabor

J. Mater. Chem. A, 2022, 10, 8273
DOI: 10.1039/D2TA00743F

Insight into the temperature-dependent lattice thermal properties of CeO2 stabilized ZrO2 by machine learning force fields

Linpo Yang, Jia Wei and Yinglin Song

Phys. Chem. Chem. Phys., 2026, 28, 2822
DOI: 10.1039/D5CP03311J

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

Steven M. Maley, Doo-Hyun Kwon, Nick Rollins, Johnathan C. Stanley, Orson L. Sydora, Steven M. Bischof and Daniel H. Ess

Chem. Sci., 2020, 11, 9665
DOI: 10.1039/D0SC03552A

OM-Diff: inverse-design of organometallic catalysts with guided equivariant denoising diffusion

François Cornet, Bardi Benediktsson, Bjarke Hastrup, Mikkel N. Schmidt and Arghya Bhowmik

Digital Discovery, 2024, 3, 1793
DOI: 10.1039/D4DD00099D

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

Elliot H. E. Farrar and Matthew N. Grayson

Chem. Sci., 2022, 13, 7594
DOI: 10.1039/D2SC02925A

Screening Diels–Alder reaction space to identify candidate reactions for self-healing polymer applications

Maxime Ferrer, Bowen Deng, Javier E. Alfonso-Ramos and Thijs Stuyver

Digital Discovery, 2025, 4, 3400
DOI: 10.1039/D5DD00340G

Unveiling the impact of ligand configurations and structural fluxionality on virtual screening of transition-metal complexes

Adarsh V. Kalikadien, Niels J. van der Lem, Cecile Valsecchi, Laurent Lefort and Evgeny A. Pidko

Digital Discovery, 2025, 4, 2033
DOI: 10.1039/D5DD00093A

Deep learning for enantioselectivity predictions in catalytic asymmetric β-C–H bond activation reactions

Ajnabiul Hoque and Raghavan B. Sunoj

Digital Discovery, 2022, 1, 926
DOI: 10.1039/D2DD00084A

Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining

Mingjian Wen, Samuel M. Blau, Xiaowei Xie, Shyam Dwaraknath and Kristin A. Persson

Chem. Sci., 2022, 13, 1446
DOI: 10.1039/D1SC06515G

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

Kjell Jorner, Tore Brinck, Per-Ola Norrby and David Buttar

Chem. Sci., 2021, 12, 1163
DOI: 10.1039/D0SC04896H

Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach

Samuel G. Espley, Elliot H. E. Farrar, David Buttar, Simone Tomasi and Matthew N. Grayson

Digital Discovery, 2023, 2, 941
DOI: 10.1039/D3DD00085K

Every atom counts: predicting sites of reaction based on chemistry within two bonds

Ching Ching Lam and Jonathan M. Goodman

Digital Discovery, 2024, 3, 1878
DOI: 10.1039/D4DD00092G

Tuning the steric hindrance of alkylamines: a predictive model of steric editing of planar amines

Michele Tomasini, Maria Voccia, Lucia Caporaso, Michal Szostak and Albert Poater

Chem. Sci., 2024, 15, 13405
DOI: 10.1039/D4SC03873H

Tailoring phosphine ligands for improved C–H activation: insights from Δ-machine learning

Tianbai Huang, Robert Geitner, Alexander Croy and Stefanie Gräfe

Digital Discovery, 2024, 3, 1350
DOI: 10.1039/D4DD00037D

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

Simone Gallarati, Raimon Fabregat, Rubén Laplaza, Sinjini Bhattacharjee, Matthew D. Wodrich and Clemence Corminboeuf

Chem. Sci., 2021, 12, 6879
DOI: 10.1039/D1SC00482D

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

Xin Yang, Arghya Bhowmik, Tejs Vegge and Heine Anton Hansen

Chem. Sci., 2023, 14, 3913
DOI: 10.1039/D2SC06696C

Predicting ruthenium catalysed hydrogenation of esters using machine learning

Challenger Mishra, Niklas von Wolff, Abhinav Tripathi, Claire N. Brodie, Neil D. Lawrence, Aditya Ravuri, Éric Brémond, Annika Preiss and Amit Kumar

Digital Discovery, 2023, 2, 819
DOI: 10.1039/D3DD00029J

Data-driven ligand field exploration of Fe(iv)–oxo sites for C–H activation

Grier M. Jones, Brett A. Smith, Justin K. Kirkland and Konstantinos D. Vogiatzis

Inorg. Chem. Front., 2023, 10, 1062
DOI: 10.1039/D2QI01961B

Predicting glycosylation stereoselectivity using machine learning

Sooyeon Moon, Sourav Chatterjee, Peter H. Seeberger and Kerry Gilmore

Chem. Sci., 2021, 12, 2931
DOI: 10.1039/D0SC06222G

Computational tools for the prediction of site- and regioselectivity of organic reactions

Lukas M. Sigmund, Michele Assante, Magnus J. Johansson, Per-Ola Norrby, Kjell Jorner and Mikhail Kabeshov

Chem. Sci., 2025, 16, 5383
DOI: 10.1039/D5SC00541H

Predicting hydrogen atom transfer energy barriers using Gaussian process regression

Evgeni Ulanov, Ghulam A. Qadir, Kai Riedmiller, Pascal Friederich and Frauke Gräter

Digital Discovery, 2025, 4, 513
DOI: 10.1039/D4DD00174E

Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity

Bo Yang, Anthony J. Schaefer, Brooke L. Small, Julie A. Leseberg, Steven M. Bischof, Michael S. Webster-Gardiner and Daniel H. Ess

Chem. Sci., 2024, 15, 18355
DOI: 10.1039/D4SC03433C

ChemSpaX: exploration of chemical space by automated functionalization of molecular scaffold

Adarsh V. Kalikadien, Evgeny A. Pidko and Vivek Sinha

Digital Discovery, 2022, 1, 8
DOI: 10.1039/D1DD00017A

A predictive journey towards trans-thioamides/amides

Michele Tomasini, Jin Zhang, Hui Zhao, Emili Besalú, Laura Falivene, Lucia Caporaso, Michal Szostak and Albert Poater

Chem. Commun., 2022, 58, 9950
DOI: 10.1039/D2CC04228B

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

Oliver Schilter, Alain Vaucher, Philippe Schwaller and Teodoro Laino

Digital Discovery, 2023, 2, 728
DOI: 10.1039/D2DD00125J

Physics-based representations for machine learning properties of chemical reactions

Puck van Gerwen, Alberto Fabrizio, Matthew D Wodrich and Clemence Corminboeuf

Mach. Learn.: Sci. Technol., 2022, 3, 045005
DOI: 10.1088/2632-2153/ac8f1a

Why Conventional Design Rules for C–H Activation Fail for Open-Shell Transition-Metal Catalysts

Aditya Nandy and Heather J. Kulik

ACS Catal., 2020, 10, 15033
DOI: 10.1021/acscatal.0c04300

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Hiroaki Okada and Satoshi Maeda

ACS Omega, 2024, 9, 7123
DOI: 10.1021/acsomega.3c09066

AABBA Graph Kernel: Atom-Atom, Bond-Bond, and Bond-Atom Autocorrelations for Machine Learning

Lucía Morán-González, Jørn Eirik Betten, Hannes Kneiding and David Balcells

J. Chem. Inf. Model., 2024, 64, 8756
DOI: 10.1021/acs.jcim.4c01583

Reinforcement Learning for Improving Chemical Reaction Performance

Ajnabiul Hoque, Mihir Surve, Shivaram Kalyanakrishnan and Raghavan B. Sunoj

J. Am. Chem. Soc., 2024
DOI: 10.1021/jacs.4c08866

Mechanistic Inference from Statistical Models at Different Data-Size Regimes

Danilo M. Lustosa and Anat Milo

ACS Catal., 2022, 12, 7886
DOI: 10.1021/acscatal.2c01741

Virtual Ligand Strategy in Transition Metal Catalysis Toward Highly Efficient Elucidation of Reaction Mechanisms and Computational Catalyst Design

Wataru Matsuoka, Yu Harabuchi and Satoshi Maeda

ACS Catal., 2023, 13, 5697
DOI: 10.1021/acscatal.3c00576

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

Yongjun Kim, Yeonjoon Kim, Hyeonsu Kim, Sungwoo Kang, Jaewook Kim, Kyunghoon Lee, Wook Jeong, Won Jong Lee, Ho Ryu, Kyungwoo Kim and Woo Youn Kim

Chemistry A European J, 2025, 31
DOI: 10.1002/chem.202500316

Rapidly tailor metal–organic frameworks for arsenate removal using graph convolutional neural networks

Zuhong Lin, Jiarong Chen, Ying Fang, Shi-hai Deng, Haipu Li, Ying Yang and Jingjing Yao

Separation and Purification Technology, 2025, 354, 129334
DOI: 10.1016/j.seppur.2024.129334

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

Michael G. Taylor, Aditya Nandy, Connie C. Lu and Heather J. Kulik

J. Phys. Chem. Lett., 2021, 12, 9812
DOI: 10.1021/acs.jpclett.1c02852

Directional multiobjective optimization of metal complexes at the billion-system scale

Hannes Kneiding, Ainara Nova and David Balcells

Nat Comput Sci, 2024, 4, 263
DOI: 10.1038/s43588-024-00616-5

ROBERT: Bridging the Gap Between Machine Learning and Chemistry

David Dalmau and Juan V. Alegre‐Requena

WIREs Comput Mol Sci, 2024, 14
DOI: 10.1002/wcms.1733

Machine Learning Transition State Geometries and Applications in Reaction Property Prediction

Isaac W. Beaglehole, Miles J. Pemberton, Elliot H. E. Farrar and Matthew N. Grayson

WIREs Comput Mol Sci, 2025, 15
DOI: 10.1002/wcms.70025

Machine Learning Parameters of Optimally Tuned Range-Separated Hybrid Functionals for Transition Metal Complexes

Xiang-Yang Liu, Qing-Xin Xiang, Sheng-Rui Wang, Dongyi Xiao, Wei-Hai Fang and Ganglong Cui

J. Phys. Chem. Lett., 2025, 16, 9672
DOI: 10.1021/acs.jpclett.5c01940

Classification of Hemilabile Ligands Using Machine Learning

Ilia Kevlishvili, Chenru Duan and Heather J. Kulik

J. Phys. Chem. Lett., 2023, 14, 11100
DOI: 10.1021/acs.jpclett.3c02828

Deconvoluting the Effects of Substituents on Reaction Barriers through Machine Learning: The Case of Brønsted Acid-Mediated Nazarov Cyclizations

Angus Keto and Elizabeth H. Krenske

J. Org. Chem., 2025, 90, 10442
DOI: 10.1021/acs.joc.5c01229

Building hybrid AI models in chemical engineering: A tutorial review

Arijit Chakraborty, Naz Pinar Taskiran, Rishab Kottooru, Vipul Mann and Venkat Venkatasubramanian

Computers & Chemical Engineering, 2025, 201, 109236
DOI: 10.1016/j.compchemeng.2025.109236

Combining Molecular Quantum Mechanical Modeling and Machine Learning for Accelerated Reaction Screening and Discovery

Nicholas Casetti, Javier E. Alfonso‐Ramos, Connor W. Coley and Thijs Stuyver

Chemistry A European J, 2023, 29
DOI: 10.1002/chem.202301957

Data‐Driven Materials Innovation and Applications

Zhuo Wang, Zhehao Sun, Hang Yin, Xinghui Liu, Jinlan Wang, Haitao Zhao, Cheng Heng Pang, Tao Wu, Shuzhou Li, Zongyou Yin and Xue‐Feng Yu

Advanced Materials, 2022, 34
DOI: 10.1002/adma.202104113

Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis

Eduardo Aguilar-Bejarano, Ender Özcan, Raja K. Rit, Hongyi Li, Hon Wai Lam, Jonathan C. Moore, Simon Woodward and Grazziela Figueredo

iScience, 2025, 28, 111881
DOI: 10.1016/j.isci.2025.111881

Automated Construction and Optimization Combined with Machine Learning to Generate Pt(II) Methane C–H Activation Transition States

Shusen Chen, Taylor Nielson, Elayna Zalit, Bastian Bjerkem Skjelstad, Braden Borough, William J. Hirschi, Spencer Yu, David Balcells and Daniel H. Ess

Top Catal, 2022, 65, 312
DOI: 10.1007/s11244-021-01506-0

Machine Learning-Guided Development of Trialkylphosphine Ni(I) Dimers and Applications in Site-Selective Catalysis

Teresa M. Karl, Samir Bouayad-Gervais, Julian A. Hueffel, Theresa Sperger, Sebastian Wellig, Sherif J. Kaldas, Uladzislava Dabranskaya, Jas S. Ward, Kari Rissanen, Graham J. Tizzard and Franziska Schoenebeck

J. Am. Chem. Soc., 2023, 145, 15414
DOI: 10.1021/jacs.3c03403

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

Sai Mahit Vadaddi, Qiyuan Zhao and Brett M. Savoie

J. Phys. Chem. A, 2024, 128, 2543
DOI: 10.1021/acs.jpca.3c07240

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

Guo‐Jin Cao

Int J of Quantum Chemistry, 2025, 125
DOI: 10.1002/qua.70036

Machine-learning models for atom-diatom reactions across isotopologues

Daniel Julian, Rian Koots and Jesús Pérez-Ríos

Phys. Rev. A, 2024, 110
DOI: 10.1103/PhysRevA.110.032811

Staged Diversity‐Constrained Machine Learning for High‐Dimensional Reaction Condition Optimization

Shu‐Wen Li, Shan Chen, João C. A. Oliveira, Shuo‐Qing Zhang, Lutz Ackermann and Xin Hong

Angew Chem Int Ed, 2026, 65
DOI: 10.1002/anie.4418883

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions

Thijs Stuyver, Kjell Jorner and Connor W. Coley

Sci Data, 2023, 10
DOI: 10.1038/s41597-023-01977-8

CaTS: Toward Scalable and Efficient Transition State Screening for Catalyst Discovery

Jun Yin, Wentao Li, Honghao Chen, Jiangjie Qiu, Huasheng Feng, Xiangya Xu, Qiuyan Jin and Xiaonan Wang

ACS Catal., 2025, 15, 15754
DOI: 10.1021/acscatal.5c03945

Benchmark of general-purpose machine learning-based quantum mechanical method AIQM1 on reaction barrier heights

Yuxinxin Chen, Yanchi Ou, Peikun Zheng, Yaohuang Huang, Fuchun Ge and Pavlo O. Dral

The Journal of Chemical Physics, 2023, 158
DOI: 10.1063/5.0137101

Discovery of Ni(I) Complexes for CO2 Insertion Enabled by a Machine Learning-Computational-Selection Sequence

Julian A. Hueffel, Mathilde Rigoulet, Sebastian Wellig, Theresa Sperger, Jas S. Ward, Kari Rissanen and Franziska Schoenebeck

J. Am. Chem. Soc., 2025, 147, 26149
DOI: 10.1021/jacs.5c00441

Molecular Design Learned from the Natural Product Porphyra-334: Molecular Generation via Chemical Variational Autoencoder versus Database Mining via Similarity Search, A Comparative Study

Yuki Harada, Makoto Hatakeyama, Shuichi Maeda, Qi Gao, Kenichi Koizumi, Yuki Sakamoto, Yuuki Ono and Shinichiro Nakamura

ACS Omega, 2022, 7, 8581
DOI: 10.1021/acsomega.1c06453

AI Approaches to Homogeneous Catalysis with Transition Metal Complexes

Lucía Morán-González, Arron L. Burnage, Ainara Nova and David Balcells

ACS Catal., 2025, 15, 9089
DOI: 10.1021/acscatal.5c01202

Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

Stefan Heinen, Guido Falk von Rudorff and O. Anatole von Lilienfeld

The Journal of Chemical Physics, 2021, 155
DOI: 10.1063/5.0059742

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

Chenru Duan, Fang Liu, Aditya Nandy and Heather J. Kulik

J. Phys. Chem. Lett., 2021, 12, 4628
DOI: 10.1021/acs.jpclett.1c00631

Integrating digital chemistry within the broader chemistry community

David Dalmau and Juan V. Alegre-Requena

Trends in Chemistry, 2024, 6, 459
DOI: 10.1016/j.trechm.2024.06.008

Understanding the Binding Properties of N‐heterocyclic Carbenes through BDE Matrix App

Lucía Morán‐González, Jaime Rodríguez‐Guerra Pedregal, Maria Besora and Feliu Maseras

Eur J Inorg Chem, 2022, 2022
DOI: 10.1002/ejic.202100932

Integrated Study on Methane Activation: Exploring Main Group Frustrated Lewis Pairs through Density Functional Theory, Machine Learning, and Machine-Learned Force Fields

Ignacio Migliaro and Thomas R. Cundari

J. Chem. Theory Comput., 2024, 20, 6388
DOI: 10.1021/acs.jctc.4c00354

Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices

David Dalmau, Susana García-Abellán and Juan V. Alegre-Requena

ACS Catal., 2026, 16, 1
DOI: 10.1021/acscatal.5c06439

Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

Gabriel dos Passos Gomes, Robert Pollice and Alán Aspuru-Guzik

Trends in Chemistry, 2021, 3, 96
DOI: 10.1016/j.trechm.2020.12.006

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

Santiago Vargas, Matthew R. Hennefarth, Zhihao Liu and Anastassia N. Alexandrova

J. Chem. Theory Comput., 2021, 17, 6203
DOI: 10.1021/acs.jctc.1c00623

Machine Learning in Predicting Activation Barrier Energy of C=N Bond Rotation in Amides

Michele Tomasini, Michal Szostak and Albert Poater

Asian J Org Chem, 2025, 14
DOI: 10.1002/ajoc.202400749

Predicting electronic structures at any length scale with machine learning

Lenz Fiedler, Normand A. Modine, Steve Schmerler, Dayton J. Vogel, Gabriel A. Popoola, Aidan P. Thompson, Sivasankaran Rajamanickam and Attila Cangi

npj Comput Mater, 2023, 9
DOI: 10.1038/s41524-023-01070-z

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

Sagar Ghorai, Ruben Staub, Yu Harabuchi, Takashi Nakano, Alexandre Varnek and Satoshi Maeda

J. Chem. Theory Comput., 2026, 22, 3516
DOI: 10.1021/acs.jctc.5c02120

HiREX: High-Throughput Reactivity Exploration for Extended Databases of Transition-Metal Catalysts

Ali Hashemi, Sana Bougueroua, Marie-Pierre Gaigeot and Evgeny A. Pidko

J. Chem. Inf. Model., 2023, 63, 6081
DOI: 10.1021/acs.jcim.3c00660

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Justin K. Kirkland, Jugal Kumawat, Maliheh Shaban Tameh, Tyson Tolman, Allison C. Lambert, Graham R. Lief, Qing Yang and Daniel H. Ess

J. Chem. Inf. Model., 2024, 64, 775
DOI: 10.1021/acs.jcim.3c01575

Predictive catalysis: a valuable step towards machine learning

Roger Monreal-Corona, Anna Pla-Quintana and Albert Poater

Trends in Chemistry, 2023, 5, 935
DOI: 10.1016/j.trechm.2023.10.005

Metal‐ligand cooperative activation of HX (X=H, Br, OR) bond on Mn based pincer complexes

Annika M. Krieger, Vivek Sinha, Adarsh V. Kalikadien and Evgeny A. Pidko

Zeitschrift anorg allge chemie, 2021, 647, 1486
DOI: 10.1002/zaac.202100078

Recent computational insights into hydrogen storage by MXene-based materials and shedding light on the storage mechanism

Turkan Kopac

Journal of Energy Storage, 2024, 97, 112807
DOI: 10.1016/j.est.2024.112807

Machine-Learning-Guided Identification of Coordination Polymer Ligands for Crystallizing Separation of Cs/Sr

Zhiyuan Zhang, Min Cheng, Xinyi Xiao, Kexin Bi, Ting Song, Kong-qiu Hu, Yiyang Dai, Li Zhou, Chong Liu, Xu Ji and Wei-qun Shi

ACS Appl. Mater. Interfaces, 2022, 14, 33076
DOI: 10.1021/acsami.2c05272

Automated Transition Metal Catalysts Discovery and Optimisation with AI and Machine Learning

Samuel Mace, Yingjian Xu and Bao N. Nguyen

ChemCatChem, 2024, 16
DOI: 10.1002/cctc.202301475

Computational Organometallic Catalysis: Where We Are, Where We Are Going

Agustí Lledós

Eur J Inorg Chem, 2021, 2021, 2547
DOI: 10.1002/ejic.202100330

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

Aditya Nandy, Chenru Duan, Michael G. Taylor, Fang Liu, Adam H. Steeves and Heather J. Kulik

Chem. Rev., 2021, 121, 9927
DOI: 10.1021/acs.chemrev.1c00347

CatalySeed: A Reaction Database for Ruthenium-Catalyzed Ethenolysis of Seed Oils with Applications in Machine Learning

Albert Poater, Susana P. García-Abellán, Juan V. Alegre-Requena, Bartosz Trzaskowski and J. Pablo Martínez

ACS Catal., 2026, 16, 2160
DOI: 10.1021/acscatal.5c06483

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations

Xabier García-Andrade, Pablo García Tahoces, Jesús Pérez-Ríos and Emilio Martínez Núñez

J. Phys. Chem. A, 2023, 127, 2274
DOI: 10.1021/acs.jpca.2c08340

A Conformer‐Dependent, Quantitative Quadrant Model

Andrew F. Zahrt, N. Ian Rinehart and Scott E. Denmark

Eur J Org Chem, 2021, 2021, 2343
DOI: 10.1002/ejoc.202100027

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Shu-Wen Li, Li-Cheng Xu, Cheng Zhang, Shuo-Qing Zhang and Xin Hong

Nat Commun, 2023, 14
DOI: 10.1038/s41467-023-39283-x

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

Aditya Nandy, Chenru Duan, Conrad Goffinet and Heather J. Kulik

JACS Au, 2022, 2, 1200
DOI: 10.1021/jacsau.2c00176

Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties

Liliana C. Gallegos, Guilian Luchini, Peter C. St. John, Seonah Kim and Robert S. Paton

Acc. Chem. Res., 2021, 54, 827
DOI: 10.1021/acs.accounts.0c00745

Specification Search in Structural Equation Modeling (SEM): How Gradient Component-wise Boosting can Contribute

Bjørn Gunnar Hansen and Ulf Henning Olsson

Structural Equation Modeling: A Multidisciplinary Journal, 2022, 29, 140
DOI: 10.1080/10705511.2021.1935263

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

L. Fiedler, K. Shah, M. Bussmann and A. Cangi

Phys. Rev. Materials, 2022, 6
DOI: 10.1103/PhysRevMaterials.6.040301

What's Left for a Computational Chemist To Do in the Age of Machine Learning?

Heather J. Kulik

Israel Journal of Chemistry, 2022, 62
DOI: 10.1002/ijch.202100016

Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport

Linfeng Yu, Kexin Dong, Qi Yang, Yi Zhang, Zheyong Fan, Xiong Zheng, Huimin Wang, Zhenzhen Qin and Guangzhao Qin

npj Comput Mater, 2024, 10
DOI: 10.1038/s41524-024-01442-z

Boosting the generality of catalytic systems by the synergetic ligand effect in Pd-catalyzed C-N cross-coupling

Nikolay O. Grebennikov, Daniil A. Boiko, Darya O. Prima, Malena Madiyeva, Mikhail E. Minyaev and Valentine P. Ananikov

Journal of Catalysis, 2024, 429, 115240
DOI: 10.1016/j.jcat.2023.115240

MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes

Kyunghoon Lee, Shinyoung Park, Minseong Park and Woo Youn Kim

J. Chem. Inf. Model., 2025, 65, 11878
DOI: 10.1021/acs.jcim.5c02074

Understanding the effectiveness of enzyme pre-reaction state by a quantum-based machine learning model

Shenggan Luo, Lanxuan Liu, Chu-Jun Lyu, Byuri Sim, Yihan Liu, Haifan Gong, Yao Nie and Yi-Lei Zhao

Cell Reports Physical Science, 2022, 3, 101128
DOI: 10.1016/j.xcrp.2022.101128

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

David Balcells and Bastian Bjerkem Skjelstad

J. Chem. Inf. Model., 2020, 60, 6135
DOI: 10.1021/acs.jcim.0c01041

Machine learning studies on asymmetric relay Heck reaction—Potential avenues for reaction development

Manajit Das, Pooja Sharma and Raghavan B. Sunoj

The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0084432

On the use of chemical bonding descriptors in machine learning

Michele Tomasini, Lucia Caporaso, Martí Gimferrer and Albert Poater

Coordination Chemistry Reviews, 2026, 550, 217383
DOI: 10.1016/j.ccr.2025.217383

Correlation between the Experimental and Theoretical Photoelectrochemical Response of a WO3 Electrode for Efficient Water Splitting through the Implementation of an Artificial Neural Network

Mamy Diaby, Asma Alimi, Afrah Bardaoui, Diogo M. F. Santos, Radhaoune Chtourou and Ibtissem Ben Assaker

Sustainability, 2023, 15, 11751
DOI: 10.3390/su151511751

Stufenweises diversitätsbeschränktes maschinelles Lernen für die Optimierung hochdimensionaler Reaktionsbedingungen

Shu‐Wen Li, Shan Chen, J. C. A. Oliveira, Shuo‐Qing Zhang, Lutz Ackermann and Xin Hong

Angewandte Chemie, 2026, 138
DOI: 10.1002/ange.4418883

Reformulating Reactivity Design for Data-Efficient Machine Learning

Toby Lewis-Atwell, Daniel Beechey, Özgür Şimşek and Matthew N. Grayson

ACS Catal., 2023, 13, 13506
DOI: 10.1021/acscatal.3c02513

Iron-Catalyzed Kumada Cross-Coupling Reaction Involving Fe8Me12– and Related Clusters: A Computational Study

Andrea Darù, Carlos Martín-Fernández and Jeremy N. Harvey

ACS Catal., 2022, 12, 12678
DOI: 10.1021/acscatal.2c03436

Co-intelligent Design of Catalysis Research with Large Language Models: Hype or Reality?

David Balcells

ACS Catal., 2025, 15, 16412
DOI: 10.1021/acscatal.5c04238

Machine intelligence for chemical reaction space

Philippe Schwaller, Alain C. Vaucher, Ruben Laplaza, Charlotte Bunne, Andreas Krause, Clemence Corminboeuf and Teodoro Laino

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1604

Dreams, False Starts, Dead Ends, and Redemption: A Chronicle of the Evolution of a Chemoinformatic Workflow for the Optimization of Enantioselective Catalysts

N. Ian Rinehart, Andrew F. Zahrt, Jeremy J. Henle and Scott E. Denmark

Acc. Chem. Res., 2021, 54, 2041
DOI: 10.1021/acs.accounts.0c00826

Indolizine Synthesis through Annulation of Pyridinium 1,4‐Thiolates and Copper Carbenes: A Predictive Catalysis Approach

Roger Monreal‐Corona, Àlex Díaz‐Jiménez, Anna Roglans, Albert Poater and Anna Pla‐Quintana

Adv Synth Catal, 2023, 365, 760
DOI: 10.1002/adsc.202201277

The relationship between activated H2 bond length and adsorption distance on MXenes identified with graph neural network and resonating valence bond theory

Jiewei Cheng, Tingwei Li, Yongyi Wang, Ahmed H. Ati and Qiang Sun

The Journal of Chemical Physics, 2023, 159
DOI: 10.1063/5.0169430

Computational mechanistic study in organometallic catalysis: Why prediction is still a challenge

Natalie Fey and Jason M. Lynam

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1590

Zuhong Lin, Siyuan Pan, Haipu Li and Jingjing Yao

Separation and Purification Technology, 2024, 340, 126649
DOI: 10.1016/j.seppur.2024.126649

Paving the road towards automated homogeneous catalyst design

Adarsh V. Kalikadien, Adrian Mirza, Aydin Najl Hossaini, Avadakkam Sreenithya and Evgeny A. Pidko

ChemPlusChem, 2024, 89
DOI: 10.1002/cplu.202300702

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Shu-Sen Chen, Zack Meyer, Brendan Jensen, Alex Kraus, Allison Lambert and Daniel H. Ess

J. Chem. Inf. Model., 2023, 63, 7412
DOI: 10.1021/acs.jcim.3c01310

Virtual Ligand-Assisted Screening Strategy to Discover Enabling Ligands for Transition Metal Catalysis

Wataru Matsuoka, Yu Harabuchi and Satoshi Maeda

ACS Catal., 2022, 12, 3752
DOI: 10.1021/acscatal.2c00267

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

Tobias Gensch, Sleight R. Smith, Thomas J. Colacot, Yam N. Timsina, Guolin Xu, Ben W. Glasspoole and Matthew S. Sigman

ACS Catal., 2022, 12, 7773
DOI: 10.1021/acscatal.2c01970

Bi-directional prediction of hydrothermal carbonization characteristics of agroforestry and livestock wastes with variable components: Graph learning model-aided waste recycle

Zi-Kai Lin, Chao-Sheng Li, Rong-Rong Cai and Yan-Qiu Long

Journal of Cleaner Production, 2024, 439, 140894
DOI: 10.1016/j.jclepro.2024.140894

A Combined DFT, Energy Decomposition, and Data Analysis Approach to Investigate the Relationship Between Noncovalent Interactions and Selectivity in a Flexible DABCOnium/Chiral Anion Catalyst System

Edward Miller, Binh Khanh Mai, Jacquelyne A. Read, William C. Bell, Jeffrey S. Derrick, Peng Liu and F. Dean Toste

ACS Catal., 2022, 12, 12369
DOI: 10.1021/acscatal.2c03077

Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors

Stefan Gugler and Markus Reiher

J. Chem. Theory Comput., 2022, 18, 6670
DOI: 10.1021/acs.jctc.2c00718

From the Felkin‐Anh Rule to the Grignard Reaction: an Almost Circular 50 Year Adventure in the World of Molecular Structures and Reaction Mechanisms with Computational Chemistry**

Odile Eisenstein

Israel Journal of Chemistry, 2022, 62
DOI: 10.1002/ijch.202100138

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

Thijs Stuyver and Connor W. Coley

The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0079574

Incorporating Anionic Ligands in Chemical Space Exploration with New Ligand Additivity Relationships

Heather J. Kulik

J. Chem. Inf. Model., 2025, 65, 6073
DOI: 10.1021/acs.jcim.5c00636

tmQMg* Data Set: Excited State Properties of 74k Transition Metal Complexes

Hannes Kneiding and David Balcells

J. Chem. Inf. Model., 2025, 65, 11766
DOI: 10.1021/acs.jcim.5c01958

Machine learning prediction of a chemical reaction over 8 decades of energy

Daniel Julian and Jesús Pérez-Ríos

Phys. Rev. Research, 2025, 7
DOI: 10.1103/gkwq-ls2k

High-Throughput Optimization of a High-Pressure Catalytic Reaction

Yusuke Tanabe, Hiroki Sugisawa, Tomohisa Miyazawa, Kazuhiro Hotta, Kazuya Shiratori and Tadahiro Fujitani

J. Chem. Inf. Model., 2025, 65, 2245
DOI: 10.1021/acs.jcim.4c02273

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis

Tobias Gensch, Gabriel dos Passos Gomes, Pascal Friederich, Ellyn Peters, Théophile Gaudin, Robert Pollice, Kjell Jorner, AkshatKumar Nigam, Michael Lindner-D’Addario, Matthew S. Sigman and Alán Aspuru-Guzik

J. Am. Chem. Soc., 2022, 144, 1205
DOI: 10.1021/jacs.1c09718

Structure-Based Relative Energy Prediction Model: A Case Study of Pd(II)-Catalyzed Ethylene Polymerization and the Electronic Effect of Ancillary Ligands

Han Lu, Xiaohui Kang and Yi Luo

J. Phys. Chem. B, 2021, 125, 12047
DOI: 10.1021/acs.jpcb.1c05143

Seeking the Optimal Descriptor for SN2 Reactions through Statistical Analysis of Density Functional Theory Results

Lucía Morán-González, Maria Besora and Feliu Maseras

J. Org. Chem., 2022, 87, 363
DOI: 10.1021/acs.joc.1c02387

Applications of density functional theory and machine learning in nanomaterials: A review

Nangamso Nathaniel Nyangiwe

Next Materials, 2025, 8, 100683
DOI: 10.1016/j.nxmate.2025.100683

Organic reactivity from mechanism to machine learning

Kjell Jorner, Anna Tomberg, Christoph Bauer, Christian Sköld and Per-Ola Norrby

Nat Rev Chem, 2021, 5, 240
DOI: 10.1038/s41570-021-00260-x

Active learning accelerates ab initio molecular dynamics on reactive energy surfaces

Shi Jun Ang, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod and Rafael Gómez-Bombarelli

Chem, 2021, 7, 738
DOI: 10.1016/j.chempr.2020.12.009

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

Daniel R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet and Heather J. Kulik

The Journal of Chemical Physics, 2022, 156
DOI: 10.1063/5.0082964

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

Jeremy J. Henle, Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Yang Wang and Scott E. Denmark

J. Am. Chem. Soc., 2020, 142, 11578
DOI: 10.1021/jacs.0c04715

Rapid Generation of Transition-State Conformer Ensembles via Constrained Distance Geometry

Stefan P. Schmid, Henrik Seng, Thibault Kläy and Kjell Jorner

J. Chem. Inf. Model., 2026, 66, 2777
DOI: 10.1021/acs.jcim.5c02794

Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases

José Peña‐Guerrero, Paul A. Nguewa and Alfonso T. García‐Sosa

WIREs Comput Mol Sci, 2021, 11
DOI: 10.1002/wcms.1513

molSimplify 2.0: Improved Structure Generation for Automating Discovery in Inorganic Molecular and Reticular Chemistry

Gianmarco G. Terrones, Roland G. St. Michel, Jacob W. Toney, Akash K. Ball, Yiran Wang, Aaron G. Garrison, Aditya Nandy, Ralf Meyer, Freya Edholm, Changhwan Oh, Sebastian G. Pujet, Daniel B. K. Chu, Davut Muhammetgulyyev and Heather J. Kulik

J. Chem. Inf. Model., 2026, 66, 2753
DOI: 10.1021/acs.jcim.5c02733

Machine learning activation energies of chemical reactions

Toby Lewis‐Atwell, Piers A. Townsend and Matthew N. Grayson

WIREs Comput Mol Sci, 2022, 12
DOI: 10.1002/wcms.1593

Generative Design of Functional Metal Complexes Utilizing the Internal Knowledge and Reasoning Capability of Large Language Models

Jieyu Lu, Zhangde Song, Qiyuan Zhao, Yuanqi Du, Yirui Cao, Haojun Jia and Chenru Duan

J. Am. Chem. Soc., 2025, 147, 32377
DOI: 10.1021/jacs.5c02097

Machine Learning for the Discovery, Design, and Engineering of Materials

Chenru Duan, Aditya Nandy and Heather J. Kulik

Annu. Rev. Chem. Biomol. Eng., 2022, 13, 405
DOI: 10.1146/annurev-chembioeng-092320-120230

Improving generative inverse design of molecular catalysts in small data regime

François Cornet, Pratham Deshmukh, Bardi Benediktsson, Mikkel N Schmidt and Arghya Bhowmik

Mach. Learn.: Sci. Technol., 2025, 6, 025057
DOI: 10.1088/2632-2153/addc32

Estimation of the Surface Free Energy Components for Solid Surfaces: A Machine Learning Approach

Ebubekir Siddik Aydin, Ibrahim Korkut and Salih Ozbay

Arab J Sci Eng, 2024, 49, 7863
DOI: 10.1007/s13369-023-08502-4

3DReact: Geometric Deep Learning for Chemical Reactions

Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne, Vignesh Ram Somnath, Ruben Laplaza, Andreas Krause and Clemence Corminboeuf

J. Chem. Inf. Model., 2024, 64, 5771
DOI: 10.1021/acs.jcim.4c00104

Enabling Automation of de Novo Catalyst Design: An Experimentally Validated, Multifactor Design Metric for Olefin Metathesis

Jonas B. Ekeli, Marco Foscato, Christian O. Blanco, Giovanni Occhipinti, Deryn E. Fogg and Vidar R. Jensen

ACS Catal., 2024, 14, 16731
DOI: 10.1021/acscatal.4c06212

Data-Driven Strategies for Accelerated Materials Design

Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, Riley J. Hickman, Mario Krenn, Cyrille Lavigne, Michael Lindner-D’Addario, AkshatKumar Nigam, Cher Tian Ser, Zhenpeng Yao and Alán Aspuru-Guzik

Acc. Chem. Res., 2021, 54, 849
DOI: 10.1021/acs.accounts.0c00785