Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
Machine-learned interatomic potentials offer near first-principles accuracy but are computationally expensive, limiting their application in large-scale molecular dynamics simulations. Inspired by quantum mechanics/molecular mechanics methods, we present ML-MIX, an efficient and flexible LAMMPS package for accelerating simulations by spatially mixing interatomic potentials of different complexities. Through constrained linear fitting, we show it is possible to generate a 'cheap' approximate model which closely matches an 'expensive' reference in relevant regions of configuration space. We demonstrate the capability of ML-MIX through case-studies in Si, Fe, and W-He systems, achieving up to an 11x speedup on 8,000 atom systems without sacrificing accuracy on static and dynamic quantities, including calculation of minimum energy paths and dynamical simulations of defect diffusion. For larger domain sizes, we show that the achievable speedup of ML-MIX simulations is limited only by the relative speed of the cheap potential over the expensive potential. The ease of use and flexible nature of this method will extend the practical reach of MLIPs throughout computational materials science, enabling parsimonious application to large spatial and temporal domains.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
BLIPs: Bayesian Learned Interatomic Potentials
Machine Learning Interatomic Potentials (MLIPs) are becoming a central tool in simulation-based chemistry. However, like most deep learning models, MLIPs struggle to make accurate predictions on out-of-distribution data …
Active LearningGraph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
Metallic magnets exhibit complex spin dynamics governed by electronically generated interactions. Predictive simulations of such dynamics typically require repeated solutions of an underlying electronic problem throughou…
Graph Neural NetworkUncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials
Efficiently creating a concise but comprehensive data set for training machine-learned interatomic potentials (MLIPs) is an under-explored problem. Active learning, which uses biased or unbiased molecular dynamics (MD) t…
Active LearningPushing the limits of unconstrained machine-learned interatomic potentials
Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures …
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing
The ability to perform fast and accurate atomistic simulations is crucial for advancing the chemical sciences. By learning from high-quality data, machine-learned interatomic potentials achieve accuracy on par with ab in…