利用语言模型,晶体结构预测和材料设计的第一原则计算
Lei Zhang1,2, Ben Ni1, Kaiyang Xu2
1Department of Internet Engineering, School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Journal of chemical information and modeling
|September 10, 2025
概括
大型语言模型与晶体结构预测和密度函数理论相结合,确定了一种新的氧化物光伏材料. 这种综合方法加速了针对定制光电子性能的反向材料设计.
科学领域:
- 凝聚物质物理学 凝聚物质物理学
- 材料科学是一种材料科学.
- 计算化学是一种计算化学.
背景情况:
- 大型语言模型 (LLM) 对材料发现有希望.
- 将LLM与晶体结构预测 (CSP) 和密度函数理论 (DFT) 等初始方法集成,对于先进的材料设计至关重要.
- 目前的方法需要改进,以便在复杂的物质空间中有效导航.
研究的目的:
- 为反向材料设计开发一个集成的计算框架.
- 将LLM指导的选与基于GA和GNN的CSP方法相结合.
- 识别具有定制光电子特性的新型光伏材料.
主要方法:
- 开发了一个综合框架,结合了LLMs,遗传算法 (GA) 和基于图形神经网络 (GNN) 的CSP.
- 利用基于变压器的向量相似性分析和无监督的集群.
- 执行第一原则计算 (DFT) 来验证材料属性.
主要成果:
- 成功识别了一种以前被忽视的具有显著光伏潜力的氧化物材料.
- 证明该材料具有直接带间隙.
- 计算出适用于光伏应用的高理论效率.
结论:
- 在LLM + CSP + DFT框架有效地导航材料空间的反向设计.
- 这种等级化的方法可以发现非直观的功能材料.
- 鉴定到的氧化物材料对未来的光伏应用非常有前途.
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