Digitized Material Design and Performance Prediction Driven by High-Throughput Computing

Jingxu Yao^1*Chuanxin Sheng²

• ¹Wuhan University of Technology, Wuhan, China
• ²Huzhou University, Huzhou, Zhejiang, China

The advancement of digitized material design has revolutionized the field of materials science by integrating computational modeling, machine learning, and high-throughput simulations.Traditional material discovery heavily relies on iterative physical experiments, which are often resource-intensive and time-consuming. Recent developments in high-throughput computing offer an efficient alternative by enabling large-scale simulations and data-driven predictions of material properties. However, conventional predictive models frequently suffer from limited generalization, inadequate incorporation of domain knowledge, and inefficient optimization of material structures.To address these limitations, we propose a novel framework that combines physics-informed machine learning with generative optimization for material design and performance prediction. Our approach consists of three major components: a graph-embedded material property prediction model that integrates multi-modal data for structure-property mapping, a generative model for structure exploration using reinforcement learning, and a physics-guided constraint mechanism that ensures realistic and reliable material designs. By embedding domain-specific priors into a deep learning framework, our method significantly improves prediction accuracy while maintaining physical interpretability. Extensive experiments demonstrate that our approach outperforms stateof-the-art models in both predictive performance and optimization efficiency, accelerating the discovery of novel materials with desired properties. This work highlights the potential of digitized design methodologies to drive next-generation material innovation.