Fine-tuning universal machine-learned interatomic potentials: A Tutorial on methods and applications

Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy. While the number of available U-MLIPs and their fine-tuning applications are rapidly expanding, there remains a lack of systematic guidance on how to effectively fine-tune these models. This Tutorial provides a comprehensive, step-by-step guide to fine-tuning U-MLIPs for computational materials modeling. Using the recently released MACE-MP-0 as a representative foundation model, we illustrate the full workflow of data set preparation, hyperparameter selection, model training, and validation. Beyond methodological…

• NN
  National Natural Science Foundation of China

  Awards: 12325113, 12426304, 12426304, 12325113, 22508245
• SA
  Science and Technology Commission of Shanghai Municipality

  Award: 23JC1402300
• NS
  Natural Science Foundation of Chongqing

  Awards: CSTB2024NSCQ, CSTB2024NSCQ-MSX1238
• CS
  Centre Scientifique et Technique du Bâtiment
• NS
  National Supercomputing Center, Korea Institute of Science and Technology Information

  Award: KSC-2024-CRE-0513