Digitized material design and performance prediction driven by high-throughput computing

Introduction 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. Methods 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. Results 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 state-of-the-art models in both predictive performance and optimization efficiency. Discussion These findings highlight the potential of digitized design methodologies to accelerate the discovery of novel materials with desired properties and to drive next-generation material innovation.