Uncertainty quantification in molecular simulations with dropout neural network potentials

Machine learning interatomic potentials(IPs)can provide accuracy close to that of first-principles methods,such as density functional theory(DFT),at a fraction of the computational cost.This greatly extends the scope of accurate molecular simulations,providing opportunities for quantitative design of materials and devices on scales hitherto unreachable by DFT methods.However,machine learning IPs have a basic limitation in that they lack a physical model for the phenomena being predicted and therefore have unknown accuracy when extrapolating outside their training set.