Conditional Generative Modeling for Amorphous Multi-Element Materials

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates stability and functionality. We develop ApolloX, a pioneering predictive framework for amorphous multi-element materials, integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution. The resulting structural evolution, governed by composition, directly impacts catalytic performance, enhancing activity and stability with increasing amorphization. This predictive-experimental synergy transforms amorphous materials discovery, unlocking new frontiers in catalysis, energy storage, and functional disordered systems.