Abstract Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a high-dimensional regression implemented by an artificial neural network with a physics-based bond-order interatomic potential applicable to both metals and nonmetals. In this paper, we present a modified version of the PINN method that accelerates the potential training process and further improves the transferability of PINN potentials to unknown atomic environments. As an application, a modified PINN potential for Al has been developed by training on a large database of electronic structure calculations. The potential reproduces the reference first-principles energies within 2.6 meV per atom and accurately predicts a wide spectrum of physical properties of Al. Such properties include, but are not limited to, lattice dynamics, thermal expansion, energies of point and extended defects, the melting temperature, the structure and dynamic properties of liquid Al, the surface tensions of the liquid surface and the solid-liquid interface, and the nucleation and growth of a grain boundary crack. Computational efficiency of PINN potentials is also discussed.
FIRE-GNN: Force-informed, Relaxed Equivariance Graph Neural Network for Rapid and Accurate Prediction of Surface Properties
Northeastern University（东北大学）；Department of Mathematics（数学系）；Khoury College of Computer Sciences（计算机科学学院）；Department of Mechanical and Industrial Engineering（机械与工业工程系）
Enhancing Heat Sink Efficiency in MOSFETs using Physics Informed Neural Networks: A Systematic Study on Coolant Velocity Estimation
Division of Applied Mathematics, Brown University, Providence, RI, USA（应用数学系，布朗大学）；MIT Sea Grant Design Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA（麻省理工学院海 grant 设计实验室）
MDcraft -- a modern molecular dynamics simulation package with machine learning potentials support
Joint Institute for High Temperatures of RAS（俄罗斯科学院高温研究所）；Dukhov Research Institute of Automatics（杜霍夫自动仪器研究 institute）；Moscow Institute of Physics and Technology（莫斯科物理技术学院）
