用AI预测无机材料稳定晶体结构，支持未知体系快速探索。
CrySPAI: A new Crystal Structure Prediction Software Based on Artificial Intelligence
- 融合进化算法、DFT和深度神经网络，实现结构搜索与能量预测协同优化。
- 通过分布式并行框架加速计算，自动化流程提升预测效率。
- 适用于新体系材料开发，尤其适合需要快速筛选的科研团队。
基于第一性原理计算与机器学习结合的晶体结构预测在材料科学中已取得显著进展。然而，多数方法仅限于特定体系，难以推广至未知或未探索领域。本文提出CrySPAI，一种基于人工智能的晶体结构预测软件，可根据化学组成预测无机材料的低能稳定晶体结构。该软件包含三大核心模块：用于搜索所有可能晶体结构配置的进化优化算法（EOA）、提供精确能量值的密度泛函理论（DFT），以及学习晶体结构与能量关系的深度神经网络（DNN）。为优化多模块协作，系统采用分布式框架实现任务并行，并集成自动化工作流以实现无缝执行。本研究展示了基于AI的CrySPAI晶体预测工具的开发与实现及其独特功能。
原文摘要 · Abstract (English)
Crystal structure predictions based on the combination of first-principles calculations and machine learning have achieved significant success in materials science. However, most of these approaches are limited to predicting specific systems, which hinders their application to unknown or unexplored domains. In this paper, we present CrySPAI, a crystal structure prediction package developed using artificial intelligence (AI) to predict energetically stable crystal structures of inorganic materials given their chemical compositions. The software consists of three key modules, an evolutionary optimization algorithm (EOA) that searches for all possible crystal structure configurations, density functional theory (DFT) that provides the accurate energy values for these structures, and a deep neural network (DNN) that learns the relationship between crystal structures and their corresponding energies. To optimize the process across these modules, a distributed framework is implemented to parallelize tasks, and an automated workflow has been integrated into CrySPAI for seamless execution. This paper reports the development and implementation of AI AI-based CrySPAI Crystal Prediction Software tool and its unique features.
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