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.
Highlights
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms
The network is trained by minimizing the error between the energies predicted by the neural network (NN) and the respective density functional theory (DFT) total energies for a large set of atomic configurations
The proposed physically informed neural network (PINN) potential model is capable of achieving the same high accuracy in interpolating between DFT energies on the potential energy surface (PES) as the currently existing mathematical NN potentials
Summary
Large-scale atomistic computer simulations of materials heavily rely on interatomic potentials predicting the energy and Newtonian forces on atoms. 1234567890():,; Large-scale molecular dynamics (MD) and Monte Carlo (MC) simulations of materials are traditionally implemented using classical interatomic potentials predicting the potential energy and Newtonian forces acting on atoms Computations with such potentials are very fast and afford access to systems with millions of atoms and MD simulation times up to hundreds of nanoseconds. Several functional forms of interatomic potentials have been developed over the years, including the embedded-atom method (EAM)[1,2,3], the modified EAM (MEAM)[4], the angular-dependent potentials[5], the charge-optimized many-body potentials[6], reactive bond-order potentials[7,8,9], and reactive force fields[10] to name a few These potentials address particular classes of materials or particular types of applications. The training process can be implemented on-the-fly by running ab initio MD simulations[26]
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