170932 20260430151735.0 doi 10.1002/chem.71022 sideral 149030 ART-2026-149030 eng Ferrer, Maxime The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy? 2026 While DFT is the computational method of choice for mechanistic insight in homogeneous catalysis, the recent rise of foundation‐level machine learning interatomic potentials (MLIPs) invites reconsideration: are we approaching competition, or a deeper synergy? These pretrained, fast surrogates are able to map reaction space, sample conformers, and flag likely transition states, potentially displacing routine low‐level DFT. Yet their reliability hinges on calibrated uncertainty, transferability across ligand and oxidation‐state manifolds, and faithful treatment of long‐range polarization, solvation, and open‐shell or multireference character. We argue that the near future will likely be contested: MLIPs will handle everyday exploratory tasks, while DFT and higher‐level methods will anchor electronic effects, validate high‐stakes predictions, and resolve edge cases. If supported by FAIR catalysis datasets, standardized workflows, and robust error quantification, the two approaches will coevolve, enabling scalable, predictive discovery without sacrificing rigor or interpretability. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/MCIU/PID2024-159030NA-I00 info:eu-repo/grantAgreement/ES/MICIU/AEI/10.13039/501100011033 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Munarriz, Julen Universidad de Zaragoza (orcid)0000-0001-6089-6126 Stuyver, Thijs Laplaza, Ruben 2012 755 Universidad de Zaragoza Dpto. Química Física Área Química Física Chemistry (Weinh.) Chemistry (Weinheim) 0947-6539 2791191 http://zaguan.unizar.es/record/170932/files/texto_completo.pdf Versión publicada 2407909 http://zaguan.unizar.es/record/170932/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:170932 articulos driver 2026-04-30-13:57:21 ARTICLE