Physical Chemistry

Choosing the right molecular machine learning potential

Authors

  • Max Pinheiro Jr Aix-Marseille University, CNRS, ICR, Marseille, France ,
  • Fuchun Ge State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial KeyLaboratory of Theoretical and Computational Chemistry, Department of Chemistry, andCollege of Chemistry and Chemical Engineering, Xiamen University ,
  • Nicolas Ferré Aix-Marseille University, CNRS, ICR, Marseille, France ,
  • Pavlo O. Dral State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial KeyLaboratory of Theoretical and Computational Chemistry, Department of Chemistry, andCollege of Chemistry and Chemical Engineering, Xiamen University ,
  • Mario Barbatti Aix-Marseille University, CNRS, ICR, Marseille, France

Abstract

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one.

Content

Thumbnail image of Choosing_the_right_molecular_machine_learning_potential.pdf

Supplementary weblinks

ML benchmark database
This is a supplementary material containing all scripts, data sets, and figures of learning curves that supports the findings of the benchmark study for machine learning interatomic potentials.