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<p>Constructing large-scale, high-quality computational materials databases is pivotal for advancing material simulation and design. However, two essential challenges are yet to be fully resolved in this field: acquiring comprehensive atomic-level structural information and effectively executing large-scale computational tasks for these material structures on supercomputers. In this study, we present a methodology that adeptly couples Artificial Intelligence (AI) and High-Performance Computing (HPC) to establish a comprehensive computational database with diverse materials data. We propose an AI-driven pipeline with a periodic-E(3)-equivariant diffusion model for structure generation and a transformer-based property prediction model incorporating 3D geometric analysis for material structure evaluation, followed by calculations on selected structures using Density Functional Theory (DFT). Specifically, a high-throughput computing framework was developed for efficient execution of various CPU/GPU/IO-intensive tasks, capitalizing on the heterogeneous computing nodes and shared storage architecture of supercomputers. Based on our HPC-AI strategy, we generated approximately 10 million hypothetical crystal structures, constituting the most extensive crystal material database currently available. By leveraging 2,000 nodes of the Tianhe-2 supercomputer for high-throughput computations, we accomplished about 80,000 DFT calculation datasets within a span of three months. Our approach represents a data-driven paradigm for boosting the materials design practice.</p>

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Star-gen: an HPC-AI framework for constructing large-scale computational materials database

  • 期刊论文

  • 发表日期：

    2025年4月7日

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  • Pin Chen,
  • Qing Mo,
  • Zexin Xu,
  • Xianwei Zhang,
  • Yutong Lu

摘要

Constructing large-scale, high-quality computational materials databases is pivotal for advancing material simulation and design. However, two essential challenges are yet to be fully resolved in this field: acquiring comprehensive atomic-level structural information and effectively executing large-scale computational tasks for these material structures on supercomputers. In this study, we present a methodology that adeptly couples Artificial Intelligence (AI) and High-Performance Computing (HPC) to establish a comprehensive computational database with diverse materials data. We propose an AI-driven pipeline with a periodic-E(3)-equivariant diffusion model for structure generation and a transformer-based property prediction model incorporating 3D geometric analysis for material structure evaluation, followed by calculations on selected structures using Density Functional Theory (DFT). Specifically, a high-throughput computing framework was developed for efficient execution of various CPU/GPU/IO-intensive tasks, capitalizing on the heterogeneous computing nodes and shared storage architecture of supercomputers. Based on our HPC-AI strategy, we generated approximately 10 million hypothetical crystal structures, constituting the most extensive crystal material database currently available. By leveraging 2,000 nodes of the Tianhe-2 supercomputer for high-throughput computations, we accomplished about 80,000 DFT calculation datasets within a span of three months. Our approach represents a data-driven paradigm for boosting the materials design practice.