AI快速生成符合目标局部环境的晶体结构，突破传统方法效率瓶颈。
AI-Assisted Rapid Crystal Structure Generation Towards a Target Local Environment
- 基于对称性设计的AI生成模型，结合机器学习描述符优化结构。
- 从25种碳同素异形体扩展至1700多种，能量均在石墨基态0.5 eV/atom内。
- 适合需模块化构建的材料设计，如金属有机框架和下一代电池材料。
在材料设计领域，传统晶体结构预测依赖于计算成本高昂的能量最小化方法，涉及力场或量子力学模拟。尽管新兴的人工智能生成模型在快速生成真实晶体结构方面展现出巨大潜力，但多数现有模型未能考虑晶体材料的独特对称性和周期性，且仅能处理每个晶胞含数十个原子以下的结构。本文提出一种名为局部环境几何导向晶体生成器（LEGO-xtal）的对称性感知AI生成方法，克服上述局限。该方法利用增强的小规模数据集训练的AI模型生成初始结构，并采用机器学习结构描述符进行优化，而非传统基于能量的方法。通过将25种已知低能sp2碳同素异形体扩展至超过1,700种，所有新结构的能量均在石墨基态能量0.5 eV/atom以内。该框架为具有模块化构建单元的材料（如金属有机框架和下一代电池材料）的靶向设计提供了通用策略。
原文摘要 · Abstract (English)
In the field of material design, traditional crystal structure prediction approaches require extensive structural sampling through computationally expensive energy minimization methods using either force fields or quantum mechanical simulations. While emerging artificial intelligence (AI) generative models have shown great promise in generating realistic crystal structures more rapidly, most existing models fail to account for the unique symmetries and periodicity of crystalline materials, and they are limited to handling structures with only a few tens of atoms per unit cell. Here, we present a symmetry-informed AI generative approach called Local Environment Geometry-Oriented Crystal Generator (LEGO-xtal) that overcomes these limitations. Our method generates initial structures using AI models trained on an augmented small dataset, and then optimizes them using machine learning structure descriptors rather than traditional energy-based optimization. We demonstrate the effectiveness of LEGO-xtal by expanding from 25 known low-energy sp2 carbon allotropes to over 1,700, all within 0.5 eV/atom of the ground-state energy of graphite. This framework offers a generalizable strategy for the targeted design of materials with modular building blocks, such as metal-organic frameworks and next-generation battery materials.
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