用生成式AI和模拟结合，高效设计能捕碳的新型金属有机框架材料
MOFA: Discovering Materials for Carbon Capture with a GenAI- and Simulation-Based Workflow
- 构建融合生成AI与多尺度模拟的全流程工作流，实现大规模材料设计
- 在450节点超算上生成的材料吸附能力跻身虚构材料库前10%，且性能随节点数线性提升
- 模块化设计适合科研人员快速部署，尤其适合材料、能源领域研究者
我们提出MOFA，一个开源的生成式AI（GenAI）与模拟相结合的工作流，用于在大规模高性能计算（HPC）系统上高通量生成金属-有机框架（MOFs）。MOFA解决了将GPU加速的生成任务与CPU/GPU优化的分子动力学、密度泛函理论及蒙特卡洛模拟筛选任务整合的关键挑战。通过在线学习框架统一异构计算任务，实现对HPC系统中CPU与GPU资源的高效利用。基于450节点（14,400个AMD Zen 3 CPU + 1800个NVIDIA A100 GPU）超算的性能测试显示，MOFA可高通量生成新型MOF结构，其二氧化碳吸附能力在假设性MOF（hMOF）数据集中排名前10；高质量材料产出量与使用节点数呈线性关系。其模块化架构便于集成至其他需动态结合生成式AI与大规模模拟的科学应用中。
原文摘要 · Abstract (English)
We present MOFA, an open-source generative AI (GenAI) plus simulation workflow for high-throughput generation of metal-organic frameworks (MOFs) on large-scale high-performance computing (HPC) systems. MOFA addresses key challenges in integrating GPU-accelerated computing for GPU-intensive GenAI tasks, including distributed training and inference, alongside CPU- and GPU-optimized tasks for screening and filtering AI-generated MOFs using molecular dynamics, density functional theory, and Monte Carlo simulations. These heterogeneous tasks are unified within an online learning framework that optimizes the utilization of available CPU and GPU resources across HPC systems. Performance metrics from a 450-node (14,400 AMD Zen 3 CPUs + 1800 NVIDIA A100 GPUs) supercomputer run demonstrate that MOFA achieves high-throughput generation of novel MOF structures, with CO$_2$ adsorption capacities ranking among the top 10 in the hypothetical MOF (hMOF) dataset. Furthermore, the production of high-quality MOFs exhibits a linear relationship with the number of nodes utilized. The modular architecture of MOFA will facilitate its integration into other scientific applications that dynamically combine GenAI with large-scale simulations.
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