The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?

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.