Sustainable Materials Discovery in the Era of Artificial Intelligence

Researchers propose ML-LCA framework to integrate machine learning-based materials discovery with lifecycle assessment for sustainable-by-design materials. The framework addresses the current inefficiency where environmental impacts are evaluated only after resources are invested in potentially unsustainable solutions.

• →Current AI materials discovery workflows optimize performance first and defer sustainability assessment until after synthesis.
• →The proposed ML-LCA framework integrates five components including materials-environment knowledge bases and multi-scale models.
• →Case studies across glass, cement, semiconductor photoresists, and polymers demonstrate both necessity and feasibility of the approach.
• →The framework enables simultaneous optimization of performance and sustainability rather than sequential assessment.
• →Implementation requires coordinated advances in data infrastructure, assessment methodologies, and regulatory alignment.