Materials "Economatics": Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning.

This Perspective discusses the application of advanced machine learning techniques to explore the latent relationships between the electrochemical performance and the environmental and socioeconomic impacts of modern nanomaterials fundamental to a carbon-neutral and sustainable future. Through the use of state-of-the-art algorithms, the aim is to make transparent the confluence of opaque factors that have resulted in the applications of nanomaterial research and development, for example, batteries, largely overlooking ecological and social consequences. We demonstrate how interpretable machine learning could uncover hidden patterns that inform more rational, holistic, and thus sustainable decision-making.