Topic
materials discovery
InvDesMobility Framework Enables Auditable Closed-Loop Materials Discovery
InvDesMobility is a novel framework that integrates multi-agent automated DFT, evidence stratification, and generative structure proposal to enable auditable closed-loop materials discovery. Over multiple iterations, it screened 2.4 million structures and retained 86 reliability-gated channels, offering a transferable feedback contract for learning from expensive calculated properties.
Artificial Intelligence Materials Discovery Lacks Environmental Assessment, Researchers Propose ML-LCA Framework
Current generative AI models for materials discovery optimize candidates solely for structural stability and functional properties, with no integration of environmental assessment. Researchers propose the ML-LCA framework, combining upstream ML-assisted discovery with downstream life cycle assessment to enable simultaneous performance-sustainability optimization. Case studies demonstrate the approach across polymers, glass, photoresists, and cement.