Sustainable Materials Discovery in the Era of Artificial Intelligence

The article discusses the integration of machine learning (ML) with life cycle assessment (LCA) to enhance sustainable materials discovery. It proposes the ML-LCA framework, which includes five components: knowledge base extraction, harmonized databases linking material properties to sustainability metrics, multi-scale modeling, ensemble predictions of manufacturing pathways, and uncertainty-aware optimization. This approach aims to address the disconnect between atomic-scale design and environmental assessment, allowing practitioners to optimize materials not just for performance but also for sustainability, thereby enabling a more holistic design process in materials engineering.