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

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Light wins uphill battle to solve enduring problem in organic synthesis

Fan, W., & Liu, G. (2025) · Nature · doi:10.1038/d41586-025-00902-w · Cited by 1

The role of shape and interaction directionality in the crystalline phase behaviour of octahedral metal-organic cages

Wolpert, E. H., Tarzia, A., Zhu, Y., & Jelfs, K. E. (2026) · Nature Communications · doi:10.1038/s41467-026-77857-7 · Cited by 1

High-Throughput Experimentation for Resource-Efficient Discovery of Methane Functionalization Catalysts

Staples, O., Mohar, J. S., & Mindiola, D. J. (2022) · ACS symposium series · doi:10.1021/bk-2022-1420.ch008 · Cited by 1

Evolutionary Algorithms and Workflows for De Novo Catalyst Design

Foscato, M., Ekeli, J. B., Costamagna, M., Grellscheid, D., & Jensen, V. R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00028-3 · Cited by 1

Using a single complex to predict the reaction energy profile: a case study of Pd/Ni-catalyzed ethylene polymerization

Han, L., Kang, X., Yu, H., Zhang, W., & Luo, Y. (2023) · Dalton Transactions · doi:10.1039/d3dt02745g · Cited by 1

OSCAR: An Extensive Repository of Chemically and Functionally Diverse OrganocatalystsPreprint

Gallarati, S., van Gerwen, P., Laplaza, R., Vela, S., Fabrizio, A., & Corminbœuf, C. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-3bmp2 · Cited by 1

Evolutionary Multiobjective Optimization of Multiligand Metal Complexes in Diverse and Vast Chemical SpacesPreprint

Kneiding, H., Nova, A., & Balcells, D. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-k3tf2 · Cited by 1

A Joint Semi-Supervised Variational Autoencoder and Transfer Learning Model for Designing Molecular Transition Metal ComplexesPreprint

Lee, Z.-H., Chen, Y., Chang, H.-T., & Yang, T. (2023) · ChemRxiv · doi:10.26434/chemrxiv-2023-0rx36 · Cited by 1

Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning

Nandy, A., & Kulik, H. J. (2024) · Wiley eBooks · doi:10.1002/9783527843435.ch20 · Cited by 1

A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Cui, T., Han, Y., Jia, H., Duan, C., & Zhao, Q. (2025) · Scientific Data · doi:10.1038/s41597-025-06350-5 · Cited by 1

Metal-Ligand Chemistry and Hybrid Nanomaterials: AI/ML for Design, Separations, and Self-Driving LaboratoriesPreprint

Advíncula, R. C., & Chen, J. (2026) · ChemRxiv · doi:10.26434/chemrxiv.15000827/v1 · Cited by 1

Derivatized phenylbenzothiazole cyclometalating ligands for reverse saturable absorption materials development

Jaffett, V. A., O’Donnell, R. M., Rohrabaugh, T. N., Harrison, J. M., Verma, N., Nguyen, C. K., & Shensky, W. M. (2022) · Organic Photonic Materials and Devices · doi:10.1117/12.2611252

Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks

Janet, J. P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1416.ch007

Quantum chemical calculations predict multicomponent reactions

(2022) · Nature Synthesis · doi:10.1038/s44160-022-00133-1

Computational Modeling of 4d and 5d Transition Metal Catalysts

Urquhart, R. J., & Tuttle, T. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00065-9

Photodynamics With Neural Networks and Kernel Ridge Regression

Marquetand, P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00071-4

Discovering metal complexes in vast chemical spaces

(2024) · Nature Computational Science · doi:10.1038/s43588-024-00618-3

Making machine learning a useful tool in the accelerated discovery of transition metal complexes

Kulik, H. J. (2019) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1439 · Cited by 49

Toward AI/ML-assisted discovery of transition metal complexes

Jin, H., & Merz, K. M. (2024) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2024.10.003 · Cited by 5

Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks

Janet, J. P. (2022) · ACS symposium series · doi:10.1021/bk-2022-1416.ch007

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Janet, J. P., Duan, C., Nandy, A., Liu, F., & Kulik, H. J. (2021) · Accounts of Chemical Research · doi:10.1021/acs.accounts.0c00686 · Cited by 57