Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon

Abstract

Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials.

17 pages, 12 figures

References in corpus (22)

• Electric Field Effect in Atomically Thin Carbon Films
• Van der Waals Density Functional for General Geometries
• Van der Waals density functionals applied to solids
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• A Higher-Accuracy van der Waals Density Functional
• On representing chemical environments
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• SchNet - a deep learning architecture for molecules and materials
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Implementation strategies in phonopy and phono3py
• Ab initio Random Structure Searching
• Machine-learning based interatomic potential for amorphous carbon
• Physically-informed artificial neural networks for atomistic modeling of materials
• An Accurate and Transferable Machine Learning Potential for Carbon
• Screened empirical bond-order potentials for Si-C
• New Superhard Carbon Phases Between Graphite and Diamond
• Machine Learning Interatomic Potential for Simulations of Carbon at Extreme Conditions
• Diamond formation from hydrocarbon mixtures in planets
• Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network method
• Development of a physically-informed neural network interatomic potential for tantalum
• Atomistic mechanism of graphene growth on SiC substrate: Large-scale molecular dynamics simulation based on a new charge-transfer bond-order type potential