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

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Redox‐active ligands as a challenge for electronic structure methods

Rastetter, U., von Wangelin, A. J., & Herrmann, C. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.27013 · Cited by 6

High entropic engineering for sustainable energy and environmental applications

Singh, M., Malik, I., Hazzazi, F., & Kumar, A. (2025) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2025.118132 · Cited by 5

MetalloGen: Automated 3D Conformer Generation for Diverse Coordination Complexes

Lee, K.-H., Park, S., Park, M. J., & Kim, W. Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02074 · Cited by 5

Mixture of experts-driven deep data mining for electrolyte additive design in zinc–iodine flow batteries

Wang, X., Chen, Q., He, N., Wang, D., Nie, B., Zhang, Y., & Liu, W. (2025) · Journal of Energy Storage · doi:10.1016/j.est.2025.119594 · Cited by 5

Adaptive Transition-State Refinement with Learned Equilibrium Flows

Darouich, S., Tong, V., Bien, T., Kästner, J., & Niepert, M. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02902 · Cited by 5

Accurate Prediction of Polymerization Performance for Metallocene Catalysts via a Dual-Path Neural Network and Local Feature Learning

Feng, J., Qin, Y., Yang, T., Fan, Y., Zhang, Y., Huang, G., Xiao, X., Chen, D., Zhao, S., & Wei, Z. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c03182 · Cited by 5

Data-Driven, Mechanistically Guided Prediction of Yield and Chemoselectivity in SuFEx Reactions

Tan, H., Gao, B., Huang, H., Xu, T., Li, Y., Dong, J., & Xue, X. (2026) · Journal of the American Chemical Society · doi:10.1021/jacs.6c04549 · Cited by 5

Substituent group tuned structural dimensionalities and magnetic properties of V-shaped bis-pyridyl ligands based Fe(II) coordination polymers

Xu, Z., Meng, S., Cao, T., Xin, Y., Wang, C., Hao, H., Yang, L., Zhou, Z., & Zhang, D. (2023) · Journal of Molecular Structure · doi:10.1016/j.molstruc.2023.136866 · Cited by 5

Prediction of hydration energies of adsorbates at Pt(111) and liquid water interfaces using machine learning

Shi, J., Zhang, X., Punyapu, V. R., & Getman, R. B. (2025) · The Journal of Chemical Physics · doi:10.1063/5.0248572 · Cited by 5

Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme

Pathirage, P. D. V. S., Quebedeaux, B., Akram, S., & Vogiatzis, K. D. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.4c05718 · Cited by 6

Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes

Finta, S., Kalikadien, A. V., & Pidko, E. A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00303 · Cited by 5

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