fair_data.py: implementing FAIR data compliance in Tribchem

TL;DR

This paper introduces a utility in TribChem that automates the creation of FAIR-compliant datasets from computational materials science data, enhancing data accessibility, reproducibility, and integration with open repositories.

Contribution

The paper presents a dedicated FAIR utility for TribChem that transforms results into standardized, machine-readable datasets, facilitating open science and data sharing in materials research.

Findings

Demonstrates FAIR utility with examples for various systems.

Enables seamless data sharing with repositories like Zenodo.

Supports reproducible and data-driven materials discovery.

Abstract

The increasing complexity and volume of data generated by high-throughput computational materials science require robust tools to ensure their accessibility, reproducibility, and reuse. In particular, integrating the FAIR Guiding Principles (Findable, Accessible, Interoperable, and Reusable) into computational workflows is essential to enable open science practices. TribChem is an open source Python software developed for the automated simulation of solid-solid interfaces using density functional theory (DFT). While TribChem already incorporates several FAIR-aligned features, we present here a dedicated FAIR utility designed to transform TribChem results into FAIR-compliant datasets. This utility comprises two tools: fair_data.py, which automatically generates standardized machine- and human-readable outputs from the TribChem database, and retrieve_data.py, which facilitates efficient data extraction through a keyword-based interface. In this paper we show the capabilities of the fair utility with examples for bulk, surface, and interface systems. The implementation allows seamless integration with public repositories such as Zenodo, paving the way for reproducible research and fostering data-driven materials discovery.