Multi-objective and cross-scale inverse design of temperature-control materials via physics-constrained machine learning
An artificial intelligence framework that incorporates a physics-constrained inverse-design system (PHICS) built upon a directed acyclic graph (DAG) architecture for CPCMs, integrating interface, phase, and carrier engineering.