Energy-GNoME: A Living Database of Selected Materials for Energy Applications

TL;DR

This paper introduces Energy-GNoME, a comprehensive database of over 33,000 energy-related materials identified using AI and ML techniques from a larger set of stable crystals, aimed at accelerating energy materials discovery.

Contribution

It presents a new database and ML-based protocol that filters and predicts key properties of energy materials, improving candidate selection efficiency.

Findings

Identified over 33,000 potential energy materials.

Developed ML models predicting thermoelectric and battery properties.

Enhanced accuracy of property predictions within applicability domains.

Abstract

Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Learning (ML) and Deep Learning (DL) tools, our protocol mitigates cross-domain data bias using feature spaces to identify potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. Classifiers with both structural and compositional features identify domains of applicability, where we expect enhanced accuracy of the regressors. Such regressors are trained to predict key materials properties like, thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage ($\Delta V_c$). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion.