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

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Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation

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Selective functionalization of hindered meta-C–H bond of o-alkylaryl ketones promoted by automation and deep learning

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Data-driven discovery of Pt single atom embedded germanosilicate MFI zeolite catalysts for propane dehydrogenation

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Machine-learning-guided reaction kinetics prediction towards solvent identification for chemical absorption of carbonyl sulfide

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Artificial intelligence pathway search to resolve catalytic glycerol hydrogenolysis selectivity

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Asymmetric Ion-Pairing in Stereoselective Vinyl Polymerization

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A machine learning approach for predicting the nucleophilicity of organic molecules

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Degradation mechanism analysis of LiNi0.5Co0.2Mn0.3O2 single crystal cathode materials through machine learning

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Automation, analytics and artificial intelligence for chemical synthesis

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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

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Trends in computational molecular catalyst design

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Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering

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Application of Machine Learning and Reaction Optimization for the Iterative Improvement of Enantioselectivity of Cinchona-Derived Phase Transfer Catalysts

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Non–von Neumann multi-input spike signal processing enabled by an artificial synaptic multiplexer

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Computer‐Driven Development of Ylide Functionalized Phosphines for Palladium‐Catalyzed Hiyama Couplings

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

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Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

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Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

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Artificial Intelligence-Driven Development of Nickel-Catalyzed Enantioselective Cross-Coupling Reactions

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Extended Connectivity Fingerprints as a Chemical Reaction Representation for Enantioselective Organophosphorus-Catalyzed Asymmetric Reaction Prediction

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Toward Ab Initio Reaction Discovery Using the Artificial Force Induced Reaction Method

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Electrosynthesis of Urea from Carbon Dioxide and Waste Nitrates: History, Recent Progress, and Future Prospects

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A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors

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Machine Learning Guided Design of Single–Phase Hybrid Lead Halide White Phosphors

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Machine learning prediction of empirical polarity using SMILES encoding of organic solvents

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3DReact: Geometric Deep Learning for Chemical Reactions

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6‐Hydroxy Picolinohydrazides Promoted Cu(I)‐Catalyzed Hydroxylation Reaction in Water: Machine‐Learning Accelerated Ligands Design and Reaction Optimization

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Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts

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Digitization of Organic Synthesis — How Synthetic Organic Chemists Use AI Technology —

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

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Decoding hexanitrobenzene (HNB) and 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as two distinctive energetic nitrobenzene compounds by machine learning

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

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

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Quantitative evaluation of collector flotation performance II: The creation of a collector property index based on molecular structure

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Machine Learning to Develop Peptide Catalysts─Successes, Limitations, and Opportunities

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Molecular Field Analysis Using Intermediates in Enantio-Determining Steps Can Extract Information for Data-Driven Molecular Design in Asymmetric Catalysis

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Decoding heterogeneous electrocatalysts for acidic oxygen evolution: mechanisms, rational design and AI acceleration

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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

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Ultra-high-throughput mapping of the chemical space of asymmetric catalysis enables accelerated reaction discovery

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Experimental and computational journey on transition-metal-catalyzed C H functionalization with fluorinated π-systems

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Transfer learning across different photocatalytic organic reactions

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A Data-Driven Framework for the Accelerated Discovery of CO2 Reduction Electrocatalysts

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Interplay of Computation and Experiment in Enantioselective Catalysis: Rationalization, Prediction, and─Correction?

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Chemoinformatic Catalyst Selection Methods for the Optimization of Copper–Bis(oxazoline)-Mediated, Asymmetric, Vinylogous Mukaiyama Aldol Reactions

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Optimization and prediction of catalysts for precise synthesis of methyl glycolate from dimethyl oxalate using machine learning coupled with particle swarm optimization algorithm

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Transition State Force Field for the Asymmetric Redox-Relay Heck Reaction

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Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

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Machine learning made easy for optimizing chemical reactions

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Contrasting Historical and Physical Perspectives in Asymmetric Catalysis: ΔΔ G ≠ versus Enantiomeric Excess

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Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates

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

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Catalyst design within asymmetric organocatalysis

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Molecular Field Analysis Using Computational-Screening Data in Asymmetric N -Heterocyclic Carbene-Copper Catalysis toward Data-Driven In Silico Catalyst Optimization

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Theoretical Perspectives in Organocatalysis

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Design and screening of transition-metal doped chalcogenides as CO2-to-CO electrocatalysts

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HTE- and AI-assisted development of DHP-catalyzed decarboxylative selenation

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ML meets MLn: Machine learning in ligand promoted homogeneous catalysis

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Recent advances of machine learning applications in the development of experimental homogeneous catalysis

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Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

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Data Science-Driven Discovery of Optimal Conditions and a Condition-Selection Model for the Chan–Lam Coupling of Primary Sulfonamides

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Integrating a multitask graph neural network with DFT calculations for site-selectivity prediction of arenes and mechanistic knowledge generation

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BatGPT-Chem: A Foundation Large Model for Chemical Engineering

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Direct Asymmetric Addition of Heteroatom Nucleophiles to Imines

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Auto Machine Learning Assisted Preparation of Carboxylic Acid by TEMPO‐Catalyzed Primary Alcohol Oxidation

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It Is Not All about the Ligands: Exploring the Hidden Potentials of tBu3P through Its Oxidative Addition Complex as the Precatalyst

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Chemical complexity challenge: Is multi‐instance machine learning a solution?

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Ligand-Based Principal Component Analysis Followed by Ridge Regression: Application to an Asymmetric Negishi Reaction

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Generalizing Performance Equations in Heterogeneous Catalysis from Hybrid Data and Statistical Learning

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Cost-informed Bayesian reaction optimization

Schoepfer, A. A., Weinreich, J., Laplaza, R., Waser, J., & Corminbœuf, C. (2024) · Digital Discovery · doi:10.1039/d4dd00225c · Cited by 25

A Unified Strategy for the Asymmetric Synthesis of Highly Substituted 1,2-Amino Alcohols Leading to Highly Substituted Bisoxazoline Ligands

Shrestha, B. L., Rose, B. T., Olen, C. L., Roth, A., Kwong, A. C., Wang, Y., & Denmark, S. E. (2021) · The Journal of Organic Chemistry · doi:10.1021/acs.joc.0c02899 · Cited by 18

Electrochemical Carbon-Ferrier Rearrangement Using a Microflow Reactor and Machine Learning-Assisted Exploration of Suitable Conditions

Sato, E. F., Tachiwaki, G., Fujii, M., Mitsudo, K., Washio, T., Takizawa, S., & Suga, S. (2023) · Organic Process Research & Development · doi:10.1021/acs.oprd.2c00267 · Cited by 19

Repurposing quantum chemical descriptor datasets for on-the-fly generation of informative reaction representations: application to hydrogen atom transfer reactions

Ramos, J. E. A., Neeser, R. M., & Stuyver, T. (2024) · Digital Discovery · doi:10.1039/d4dd00043a · Cited by 17

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis

Schmid, S. P., Schlosser, L., Glorius, F., & Jorner, K. (2024) · Beilstein Journal of Organic Chemistry · doi:10.3762/bjoc.20.196 · Cited by 21

Decoding the Nested, Multicycle Mechanism of Ni-Catalyzed Redox-Neutral Cross-Coupling through Temperature Scanning Reaction Calorimetry

Cagan, D. A., Péter, Á., Vokits, B., Chan, M., Baran, P. S., & Blackmond, D. G. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c15506 · Cited by 20

Open questions on the transfer of chirality

Reid, J. P. (2021) · Communications Chemistry · doi:10.1038/s42004-021-00614-y · Cited by 17

Chemical Reactivity Prediction: Current Methods and Different Application Areas

Ertl, P., Gerebtzoff, G., Lewis, R. A., Münkler, H., Schneider, N., Sirockin, F., Stiefl, N., & Tosco, P. (2021) · Molecular Informatics · doi:10.1002/minf.202100277 · Cited by 18

Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks

Nakamura, T., Sakaue, S., Fujii, K., Harabuchi, Y., Maeda, S., & Iwata, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-04967-9 · Cited by 17

Machine Learning Prediction of Structure‐Performance Relationship in Organic Synthesis

Yang, L., Zhu, L., Zhang, S., & Hong, X. (2022) · Chinese Journal of Chemistry · doi:10.1002/cjoc.202200039 · Cited by 17

COBRA web application to benchmark linear regression models for catalyst optimization with few-entry datasets

Cao, Z. Z., Falivene, L., Poater, A., Maity, B., Zhang, Z., Takasao, G., Sayed, S. B., Petta, A., Talarico, G., Oliva, R., et al. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.102348 · Cited by 17

Machine Learning Prediction of Surface Segregation Energies on Low Index Bimetallic Surfaces

Ologunagba, D., & Kattel, S. P. (2020) · Energies · doi:10.3390/en13092182 · Cited by 17

Active learning for advanced biodegradable film design

Gao, Y., Liu, C., Zhao, Y., Zhao, P., Chen, F., Li, Q., Zhang, Y., Yang, B., & Zhou, H. (2025) · Nexus · doi:10.1016/j.ynexs.2025.100070 · Cited by 16

Large Language Models Assisted Materials Development: Case of Predictive Analytics for Oxygen Evolution Reaction Catalysts of (Oxy)hydroxides

Wei, C., Shi, Y., Mu, W., Zhang, H., Qin, R., Yin, Y., Yu, G., & Mu, T. (2025) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c00798 · Cited by 17

Machine Learning Prediction of CO Adsorption Energies and Properties of Layered Alloys Using an Improved Feature Selection Algorithm

Shi, T.-T., Liu, G.-Y., & Chen, Z. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c09020 · Cited by 16

Empowering research in chemistry and materials science through intelligent algorithms

Lin, J., & Mo, F. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100035 · Cited by 16

Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes

Harada, S., Takenaka, H., Ito, T., Kanda, H., & Nemoto, T. (2024) · Nature Communications · doi:10.1038/s41467-024-46523-1 · Cited by 16

Predicting catalyst selectivity

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

Primerjalna študija načinov prikazovanja reakcij pri modeliranju selektivnosti v asimetrični katalizi

Godec, N. (2026) · Repository of the University of Ljubljana (University of Ljubljana)

Machine learning for asymmetric catalysis

Yeston, J. (2020) · Science · doi:10.1126/science.367.6478.638-e

Machine learning models for catalytic asymmetric reactions of simple alkenes: from enantioselectivity predictions to chemical insights

Hoque, A., Jain, N., Chenna, D. R., & Sunoj, R. B. (2026) · Digital Discovery · doi:10.1039/d5dd00483g · Cited by 4

Computational methods for asymmetric catalysis

Pinus, S., Genzling, J., Patrascu, M. B., & Moitessier, N. (2024) · Nature Catalysis · doi:10.1038/s41929-024-01258-6 · Cited by 16