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. By presenting a case study to explore relationships within a publicly available battery compound data set, we propose a framework that expands on existing methods, such as life cycle analysis and criticality assessments. This framework broadens the scope of nanomaterial understanding by incorporating increasingly holistic factors, while also enhancing scalability and explanatory capacity. Ultimately, using this approach, practitioners will be able to identify and analyze the fundamental barriers that are hindering the renewable energy transition, thus contributing to the future of sustainable nanomaterial research and development.