通过大型语言模型准确预测3D晶体结构的可合成性和前体
Zhilong Song1, Shuaihua Lu1, Minggang Ju1
1Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, China.
Nature communications
|July 15, 2025
概括
预测晶体结构的合成性对于材料科学至关重要. 一个新的水晶合成大型语言模型 (CSLLM) 框架准确地预测了合成性,方法和前体,加速了材料的发现.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 合成性预测对于将理论材料转化为实际应用至关重要.
- 目前依赖热力学和动力学稳定的方法在准确预测现实世界的合成性方面存在局限性.
- 在理论预测和新型晶体结构的实验可行性之间存在差距.
研究的目的:
- 开发一个先进的框架,晶体合成大型语言模型 (CSLLM),用于预测晶体结构的合成性.
- 为新材料确定潜在的合成方法和合适的前体.
- 弥合理论材料发现和实验实现之间的差距.
主要方法:
- 开发了水晶合成大型语言模型 (CSLLM) 框架,包括三个专门的大型语言模型 (LLM).
- 构建可合成和不可合成晶体结构的综合数据集.
- 为3D晶体结构创建高效的文本表示,以微调LLMs.
- 使用图形神经网络来预测材料属性.
主要成果:
- 合成性LLM实现了最先进的准确性 (98.6%),显著超过了传统的基于稳定性的选.
- 方法和前体LLM在预测合成方法和固态前体方面表现出超过90%的准确性.
- 该CSLLM框架显示出超出培训数据的复杂实验结构的优秀概括能力.
- 数以万计的可合成的理论结构被确定,预测了23个关键性质.
结论:
- CSLLM框架提供了一种强大而准确的方法来预测晶体结构合成性,合成路径和前体.
- 这种人工智能驱动的方法通过有效选大量理论结构来加速新材料的发现.
- 该研究强调了大型语言模型在推进材料科学和促进实验验证方面的潜力.
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