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Deep learning of experimental electrochemistry for battery cathodes across diverse compositions. (arXiv:2304.04986v2 [cond-mat.mtrl-sci] UPDATED)

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Artificial intelligence (AI) has emerged as a tool for discovering and
optimizing novel battery materials. However, the adoption of AI in battery
cathode representation and discovery is still limited due to the complexity of
optimizing multiple performance properties and the scarcity of high-fidelity
data. In this study, we present a comprehensive machine-learning model (DRXNet)
for battery informatics and demonstrate the application in the discovery and
optimization of disordered rocksalt (DRX) cathode materials. We have compiled
the electrochemistry data of DRX cathodes over the past five years, resulting
in a dataset of more than 30,000 discharge voltage profiles on diverse
chemistries spanning 14 different metal species. Learning from this extensive
dataset, our DRXNet model can automatically capture critical features in the
cycling curves of DRX cathodes under various conditions. Illustratively, the
model gives rational predictions of the discharge capacity for diverse
compositions in the Li--Mn--O--F chemical space as well as for high-entropy
systems. As a universal model trained on diverse chemistries, our approach
offers a data-driven solution to facilitate the rapid identification of novel
cathode materials, accelerating the development of next-generation batteries
for carbon neutralization.

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