Benchmarking 34 OpenKIM Nickel Potentials with an Emphasis on Surfaces and Extended Defects

TL;DR

This paper introduces an automated benchmarking framework for FCC nickel potentials, evaluating 47 metrics across various properties and scenarios, revealing strengths and limitations of different models, and guiding future potential development.

Contribution

The study provides a comprehensive, reproducible benchmarking suite for nickel potentials, systematically comparing 34 models and identifying their accuracy trade-offs and systematic limitations.

Findings

SNAP models achieve lowest errors across metrics

Embedded-atom potentials are competitive in many areas

Migration and short-range physics are less accurately predicted

Abstract

We present an automated benchmarking suite for face-centered-cubic (FCC) nickel that evaluates 47 quantitative metrics spanning both standard tests (equation of state, elastic constants, surface energies and phonons) and application-specific scenarios such as defect formation and migration, grain boundaries, step edges, close-range interactions, and vacancy cluster energetics. Using this framework, we assess 34 interatomic potentials from the OpenKIM repository, including pairwise, embedded-atom, modified-embedded-atom, angular-dependent, and spectral neighbor analysis potentials (SNAP). Results are compared against ab initio benchmarks compiled from the literature. Most potentials accurately reproduce lattice parameters, elastic constants, and surface energies, whereas predictive accuracy degrades for migration barriers and short-range compression. Principal-component analysis identifies correlated property groups and a partially orthogonal component associated with migration and short-range physics, revealing Pareto trade-offs between accuracy domains. SNAP models occupy the lowest-error frontier, although several embedded-atom potentials remain competitive across many metrics. The framework provides a reproducible baseline for potential selection, highlights systematic limitations across formalisms, and supports benchmarking-in-the-loop strategies for developing next-generation machine-learning potentials for Ni and Ni-based alloys.