用扩散模型生成超大规模金属有机框架，实现原子级精准设计
Mofasa: A Step Change in Metal-Organic Framework Generation
- 基于全原子潜在空间的扩散模型，联合采样原子位置、类型和晶格向量
- 可生成最大500原子的MOF结构，性能达当前最佳水平
- 开源百万级结构库与交互式检索平台，助力材料发现
Mofasa 是一种全原子潜在扩散模型，在金属有机框架（MOFs）生成任务中达到领先性能。这类多孔晶体材料可用于从沙漠空气中取水、捕获二氧化碳、储存有毒气体及催化化学反应。由于其重要性，相关研究近期荣获诺贝尔化学奖。尽管MOFs具有可理性设计、组合空间庞大且结构-性能关联强等优势，适合利用生成模型进行探索，但迄今仍缺乏高性能生成模型。为此，我们提出 Mofasa，一种通用的潜在扩散模型，能够对最多包含500个原子的系统，联合采样原子位置、原子类型和晶格向量。该方法摒弃了文献中常见的手工组装算法，实现了金属节点、连接体与拓扑结构的同步发现。为促进社区发展，我们发布了 MofasaDB——一个包含数十万条标注结构的数据库，并提供了友好的网页界面用于搜索与发现：https://mofux.ai/。
原文摘要 · Abstract (English)
Mofasa is an all-atom latent diffusion model with state-of-the-art performance for generating Metal-Organic Frameworks (MOFs). These are highly porous crystalline materials used to harvest water from desert air, capture carbon dioxide, store toxic gases and catalyse chemical reactions. In recognition of their value, the development of MOFs recently received a Nobel Prize in Chemistry. In many ways, MOFs are well-suited for exploiting generative models in chemistry: they are rationally-designable materials with a large combinatorial design space and strong structure-property couplings. And yet, to date, a high performance generative model has been lacking. To fill this gap, we introduce Mofasa, a general-purpose latent diffusion model that jointly samples positions, atom-types and lattice vectors for systems as large as 500 atoms. Mofasa avoids handcrafted assembly algorithms common in the literature, unlocking the simultaneous discovery of metal nodes, linkers and topologies. To help the scientific community build on our work, we release MofasaDB, an annotated library of hundreds of thousands of sampled MOF structures, along with a user-friendly web interface for search and discovery: https://mofux.ai/ .
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