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Citation: G.P. Purja Pun, V. Yamakov, J. Hickman, E.H. Glaessgen, and Y. Mishin (2020), "Development of a general-purpose machine-learning interatomic potential for aluminum by the physically informed neural network method", Physical Review Materials, 4(11), 113807. DOI: 10.1103/physrevmaterials.4.113807.
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.
Citation: G.P. Purja Pun, R. Batra, R. Ramprasad, and Y. Mishin (2019), "Physically-informed artificial neural networks for atomistic modeling of materials", Nature Communications, 10(1), 2339. DOI: 10.1038/s41467-019-10343-5.
Abstract: Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the 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 within 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. This approach, called the physically informed neural network (PINN) potential, is demonstrated by developing a general-purpose PINN potential for Al. We suggest that the development of physics-based ML potentials is the most effective way forward in the field of atomistic simulations.

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Notes: This file was obtained from https://github.com/ymishin-gmu/LAMMPS-USER-PINN on April 13, 2022 and posted with Yuri Mishin's permission. The repository found at the same link contains a copy of the LAMMPS source code that can be used to build a LAMMPS executable that works with this potential.
Date Created: October 5, 2010 | Last updated: June 09, 2022