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

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

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

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

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

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

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Quantum Chemical Roots of Machine-Learning Molecular Similarity Descriptors

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The Role of AI in Driving the Sustainability of the Chemical Industry

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

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

Cornet, F., Benediktsson, B., Hastrup, B., Schmidt, M. N., & Bhowmik, A. (2024) · Digital Discovery · doi:10.1039/d4dd00099d · Cited by 19 · see citing works

On the use of chemical bonding descriptors in machine learning

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

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Building hybrid AI models in chemical engineering: A tutorial review

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

Tomasini, M., Voccia, M., Caporaso, L., Szostak, M., & Poater, A. (2024) · Chemical Science · doi:10.1039/d4sc03873h · Cited by 17 · see citing works

Machine Learning in Homogeneous Catalysis: Basic Concepts and Best Practices

Dalmau, D., García‐Abellán, S., & Alegre-Requena, J. V. (2025) · ACS Catalysis · doi:10.1021/acscatal.5c06439 · Cited by 18 · see citing works

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

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

Kalikadien, A. V., Pidko, E. A., & Sinha, V. (2022) · Digital Discovery · doi:10.1039/d1dd00017a · Cited by 15 · see citing works

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

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Classification of Hemilabile Ligands Using Machine Learning

Kevlishvili, I., Duan, C., & Kulik, H. J. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c02828 · Cited by 15 · see citing works

Reinforcement Learning for Improving Chemical Reaction Performance

Hoque, A., Surve, M., Kalyanakrishnan, S., & Sunoj, R. B. (2024) · Journal of the American Chemical Society · doi:10.1021/jacs.4c08866 · Cited by 15 · see citing works

A Conformer‐Dependent, Quantitative Quadrant Model

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

Cheng, J., Li, T., Wang, Y., Ati, A. H., & Sun, Q. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0169430 · Cited by 14 · see citing works

A predictive journey towards trans -thioamides/amides

Tomasini, M., Zhang, J., Zhao, H. C., Besalú, E., Falivene, L., Caporaso, L., Szostak, M., & Poater, A. (2022) · Chemical Communications · doi:10.1039/d2cc04228b · Cited by 14 · see citing works

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

Diaby, M., Alimi, A., Bardaoui, A., Santos, D. M. F., Chtourou, R., & Assaker, I. B. (2023) · Sustainability · doi:10.3390/su151511751 · Cited by 14 · see citing works

Reformulating Reactivity Design for Data-Efficient Machine Learning

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

Hueffel, J. A., Rigoulet, M., Wellig, S., Sperger, T., Ward, J. S., Rissanen, K. T., & Schoenebeck, F. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c00441 · Cited by 13 · see citing works

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

Krieger, A. M., Sinha, V., Kalikadien, A. V., & Pidko, E. A. (2021) · Zeitschrift für anorganische und allgemeine Chemie · doi:10.1002/zaac.202100078 · Cited by 13 · see citing works

Dynamic mesophase transition induces anomalous suppressed and anisotropic phonon thermal transport

Yu, L., Dong, K., Yang, Q., Zhang, Y., Fan, Z., Zheng, X., Wang, H., Qin, Z., & Qin, G. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01442-z · Cited by 12 · see citing works

Seeking the Optimal Descriptor for S N 2 Reactions through Statistical Analysis of Density Functional Theory Results

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

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Integrating digital chemistry within the broader chemistry community

Dalmau, D., & Alegre‐Requena, J. V. (2024) · Trends in Chemistry · doi:10.1016/j.trechm.2024.06.008 · Cited by 11 · see citing works

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

Lin, Z., Chen, J., Fang, Y. Z., Deng, S., Li, H., Yang, Y., & Yao, J. (2024) · Separation and Purification Technology · doi:10.1016/j.seppur.2024.129334 · Cited by 11 · see citing works

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

Yang, B., Schaefer, A. J., Small, B. L., Leseberg, J. A. H., Bischof, S. M., Webster‐Gardiner, M. S., & Ess, D. H. (2024) · Chemical Science · doi:10.1039/d4sc03433c · Cited by 11 · see citing works

The tmQM Dataset - Quantum Geometries and Properties of 86k Transition Metal ComplexesPreprint

Balcells, D., & Skjelstad, B. B. (2020) · ChemRxiv · doi:10.26434/chemrxiv.12894818 · Cited by 20 · see citing works

Predicting ruthenium catalysed hydrogenation of esters using machine learning

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

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Machine Learning Reactivity in the Chemical Space Surrounding Vaska's ComplexPreprint

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Application of Machine Learning in Chemical Space: Machine Learning Methods Applied to Autocorrelation Vectors Generated from the Vaska’s Space Dataset.

Betten, J. E. (2023) · NORA - Norwegian Open Research Archives

Exploration of Chemical Space by Machine Learning

Sosnin, S. (2021) · Figshare · doi:10.6084/m9.figshare.14160683 · Cited by 1 · see citing works

Towards a chemistry genome

Bligaard, T., & Hinrichsen, B. T. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01059-w

MACHINE LEARNING-ENABLED EXPERIMENT-DRIVEN CARTOGRAPHY OF COMPLEX CHEMICAL SPACES

WEI, L. Y. (2026) · National University of Singapore