Materials “Economatics”: Combining Chemical, Financial, Environmental, and Social Factors Using Machine Learning

ACS Nano, Год журнала: 2025, Номер unknown

Опубликована: Март 7, 2025

This Perspective discusses the application of advanced machine learning techniques to explore latent relationships between electrochemical performance and environmental socioeconomic impacts modern nanomaterials fundamental a carbon-neutral sustainable future. Through use state-of-the-art algorithms, aim is make transparent confluence opaque factors that have resulted in applications nanomaterial research development, for example, batteries, largely overlooking ecological social consequences. We demonstrate how interpretable could uncover hidden patterns inform more rational, holistic, thus decision-making. By presenting case study within publicly available battery compound data set, we propose framework expands on existing methods, such as life cycle analysis criticality assessments. broadens scope understanding by incorporating increasingly holistic factors, while also enhancing scalability explanatory capacity. Ultimately, using this approach, practitioners will be able identify analyze barriers are hindering renewable energy transition, contributing future development.

Язык: Английский

Multi-Objective optimization of material properties for enhanced battery performance using artificial Intelligence

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 128179 - 128179

Опубликована: Май 1, 2025

Язык: Английский