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

arXiv: https://arxiv.org/abs/2411.10125 · HTML: https://mlsanity.com/paper/2411.10125/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 83 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials](https://mlsanity.com/paper/2101.03164/) (2021)
- [Classification-based detection and quantification of cross-domain data bias in materials discovery](https://mlsanity.com/paper/2311.09891/) (2023)

## Built on by (most cited first, 1 of 1)

- [Generative Models for Crystalline Materials](https://mlsanity.com/paper/2511.22652/) (2025)

## Abstract (arXiv)

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 Vc). 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.