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.

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model.This severely limits the practical application of these models in systems with dynamics governed by important rare events,such as chemical reactions and diffusion.We present an adaptive Bayesian inference method for automating the training of interpretable,low-dimensional,and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework,the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model.The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency,while requiring a minimal amount of ab initio training data.We provide a fully opensource implementation of our method,as well as a procedure to map trained models to computationally efficient tabulated force fields.

Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture

Recently,machine learning(ML)has been used to address the computational cost that has been limiting ab initio molecular dynamics(AIMD).Here,we present GNNFF,a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant,but rotationally-covariant to the coordinate of the atoms.We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems,but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system.Finally,we use our framework to perform an MD simulation of Li7P3S11,a superionic conductor,and show that resulting Li diffusion coefficient is within 14%of that obtained directly from AIMD.The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.

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.