High-throughput assessment of vacancy formation and surface energies of materials using classical force-fields

TL;DR

This paper introduces a comprehensive open-access database of surface and vacancy formation energies calculated with classical force-fields, validated against DFT and experiments, aiding materials research and defect analysis.

Contribution

It provides the first large-scale, validated database of surface and vacancy energies using classical force-fields, facilitating defect and surface property investigations.

Findings

Database contains 17,506 surface energies and 1,000 vacancy energies.

Validated FF results against DFT and experimental data.

Demonstrated applications in nanoparticle shape prediction and diffusion analysis.

Abstract

In this work, we present an open access database for surface and vacancy-formation energies using classical force-fields (FFs). These quantities are essential in understanding diffusion behavior, nanoparticle formation and catalytic activities. FFs are often designed for a specific application, hence, this database allows the user to understand whether a FF is suitable for investigating particular defect and surface-related material properties. The FF results are compared to density functional theory and experimental data whenever applicable for validation. At present, we have 17,506 surface energies and 1,000 vacancy formation energies calculation in our database and the database is still growing. All the data generated, and the computational tools used, are shared publicly at the following websites https://www.ctcms.nist.gov/~knc6/periodic.html, https://jarvis.nist.gov and https://github.com/usnistgov/jarvis . Approximations used during the high-throughput calculations are clearly mentioned. Using some of the example cases, we show how our data can be used to directly compare different FFs for a material and to interpret experimental findings such as using Wulff construction for predicting equilibrium shape of nanoparticles. Similarly, the vacancy formation energies data can be useful in understanding diffusion related properties.