The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design

TL;DR

JARVIS is an open-access, integrated database and toolkit that accelerates materials discovery by combining DFT, force-fields, and machine learning, with extensive datasets and analysis tools.

Contribution

This work introduces the comprehensive JARVIS platform, integrating diverse computational materials data and tools to facilitate data-driven materials design and discovery.

Findings

Contains 40,000 materials and 1 million properties in JARVIS-DFT

Includes 1,500 materials and 110 force-fields in JARVIS-FF

Provides 25 machine learning models for property prediction

Abstract

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of 40,000 materials and 1 million calculated properties in JARVIS-DFT, 1,500 materials and 110 force-fields in JARVIS-FF, and 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov .