A "Magnetic" Machine Learning Interatomic Potential for Nickel

Abstract

Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a "magnetism-hidden" machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The "magnetism-hidden" DP-Ni model exhibits high transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex mechanical behavior and serves as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.

References in corpus (12)

• Necessary and Sufficient Elastic Stability Conditions in Various Crystal Systems
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• A Performance and Cost Assessment of Machine Learning Interatomic Potentials
• Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
• Deep Potentials for Materials Science
• End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
• Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and FCC Metals
• Automated discovery of a robust interatomic potential for aluminum
• Gaussian approximation potentials for body-centered-cubic transition metals
• Solute-point defect interactions, coupled diffusion, and radiation induced segregation in fcc nickel
• Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron
• Searching for iron nanoparticles with a general-purpose Gaussian approximation potential