Physically-informed artificial neural networks for atomistic modeling of materials

TL;DR

This paper introduces a physically-informed neural network potential that combines physics-based models with machine learning to improve transferability and accuracy in atomistic simulations of materials, demonstrated on aluminum.

Contribution

The paper presents a novel PINN potential that integrates analytical bond-order potentials with neural networks, enhancing transferability and accuracy over traditional ML potentials.

Findings

Achieves DFT-level energy prediction accuracy.

Provides excellent agreement with experimental data.

Demonstrates broad applicability to physical properties of Al.

Abstract

Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the potential energy and Newtonian forces on atoms. Traditional interatomic potentials are based on physical intuition but contain few adjustable parameters and are usually not accurate. The emerging machine-learning (ML) potentials achieve highly accurate interpolation between the energies in a large DFT database but, being purely mathematical constructions, suffer from poor transferability to unknown structures. We propose a new approach that can drastically improve the transferability of ML potentials by informing them of the physical nature of interatomic bonding. This is achieved by combining a rather general physics-based model (analytical bond-order potential) with a neural-network regression. The network adjusts the parameters of the physics-based model on the fly during the simulations according to the local environments of individual atoms. This approach, called the physically-informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. The potential provides a DFT-level accuracy of energy predictions and excellent agreement with experimental and DFT data for a wide range of physical properties. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of atomistic simulations.