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

Area of Science:

• Materials Science and Engineering
• Computational Science
• Sustainability Studies

Background:

• Modern nanomaterials are crucial for a sustainable future, particularly in energy storage like batteries.
• Current research often overlooks the environmental and socioeconomic consequences of nanomaterial development.
• Opaque factors in research and development hinder a holistic understanding of sustainability.

Purpose of the Study:

• To apply advanced machine learning (ML) to uncover latent relationships between electrochemical performance and sustainability impacts of nanomaterials.
• To develop a framework for more rational, holistic, and sustainable decision-making in nanomaterial research and development.
• To identify barriers hindering the renewable energy transition through data-driven insights.

Main Methods:

• Utilized state-of-the-art machine learning algorithms to analyze complex datasets.
• Employed interpretable ML techniques to reveal hidden patterns and correlations.
• Conducted a case study using a public battery compound dataset, expanding on life cycle analysis and criticality assessments.

Main Results:

• Demonstrated the capability of interpretable ML to identify previously unrecognized links between material properties and broader impacts.
• Proposed a novel framework that integrates environmental and socioeconomic factors into nanomaterial assessment.
• The framework enhances scalability and explanatory capacity beyond traditional methods.

Conclusions:

• Advanced ML offers a powerful approach to understanding the multifaceted impacts of nanomaterials.
• This framework facilitates more informed and sustainable decision-making in the field.
• The approach can help overcome critical barriers to the renewable energy transition and advance sustainable nanomaterial research.