Energy-GNoME: A Living Database of Selected Materials for Energy Applications
This work accelerates materials discovery for energy applications like electricity generation and storage, but it is incremental as it builds on existing GNoME data and methods.
The researchers tackled the problem of discovering materials for energy applications by creating the Energy-GNoME database, which identifies over 33,000 potential energy materials from a larger set of 380,000 stable crystals, using machine learning to predict properties like thermoelectric figure of merit and band gap.
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 ($ΔV_c$). 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.