Sustainable Materials Discovery in the Era of Artificial Intelligence

2026-01-29

AI Summary

This study addresses the critical gap in current AI-driven materials discovery, which predominantly prioritizes performance optimization while deferring sustainability assessment to post-synthesis stages, thereby failing to account for full life-cycle environmental impacts at the atomic scale. To overcome this limitation, the work proposes the first unified machine learning–life cycle assessment (ML-LCA) framework that integrates multi-scale modeling, uncertainty-aware manufacturing pathway prediction, and sustainability metrics to enable concurrent optimization of performance and environmental impact. The authors construct a materials–environment knowledge base and a property–sustainability correlation database, demonstrating the feasibility and necessity of a “design-for-sustainability” paradigm across diverse material systems—including glasses, cements, photoresists, and polymers—while uncovering material-class-specific integration challenges.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current AI workflows optimize performance first, deferring sustainability to post synthesis assessment. This creates inefficiency by the time environmental burdens are quantified, resources have been invested in potentially unsustainable solutions. The disconnect between atomic scale design and lifecycle assessment (LCA) reflects fundamental challenges, data scarcity across heterogeneous sources, scale gaps from atoms to industrial systems, uncertainty in synthesis pathways, and the absence of frameworks that co-optimize performance with environmental impact. We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment. The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization enabling simultaneous performance-sustainability navigation. Case studies spanning glass, cement, semiconductor photoresists, and polymers demonstrate both necessity and feasibility while identifying material-specific integration challenges. Realizing ML-LCA demands coordinated advances in data infrastructure, ex-ante assessment methodologies, multi-objective optimization, and regulatory alignment enabling the discovery of materials that are sustainable by design rather than by chance.

Problem

• sustainable materials discovery
• artificial intelligence
• lifecycle assessment
• multi-scale modeling
• uncertainty quantification

Innovation

• machine learning
• life cycle assessment
• sustainable materials
• multi-scale modeling
• uncertainty-aware optimization