Choosing the right molecular machine learning potential

16 August 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


potential energy surfaces
neural network
kernel methods
molecular descriptors
data-driven approach
molecular dynamics

Supplementary weblinks


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