Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC

Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been recently developed to train force fields efficiently and automatically.Among them,Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions.In this work,we present a general Bayesian active learning workflow,where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors.To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation,we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude.We demonstrate the autonomous active learning workflow by training a Bayesian force field model for silicon carbide(SiC)polymorphs in only a few days of computer time and show that pressure-induced phase transformations are accurately captured.The resulting model exhibits close agreement with both ab initio calculations and experimental measurements,and outperforms existing empirical models on vibrational and thermal properties.The active learning workflow readily generalizes to a wide range of material systems and accelerates their computational understanding.

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features.This allows for automated active learning of models combining near-quantum accuracy,built-in uncertainty,and constant cost of evaluation that is comparable to classical analytical models,capable of simulating millions of atoms.Using this approach,we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer.We discover an unusual phase transformation mechanism of 2D stanene,where ripples lead to nucleation of bilayer defects,densification into a disordered multilayer structure,followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature.The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.