数据驱动的基于分数的模型用于生成具有自适应晶体细胞的稳定结构
Arsen Sultanov1, Jean-Claude Crivello1,2, Tabea Rebafka3,4
1Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 rue Henri Dunant, 94320 Thiais, France.
Journal of chemical information and modeling
|November 10, 2023
概括
这项研究引入了一种新的机器学习模型,用于生成新的,稳定的晶体结构. 该方法独特地学习并产生晶格,使得具有特定性质的材料的设计成为可能.
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
- 机器学习 机器学习
背景情况:
- 发现新的功能性和稳定的材料是复杂和具有挑战性的.
- 由于周期性和对称性约束,生成晶体结构存在独特的困难.
研究的目的:
- 开发一种机器学习生成模型,用于创建具有化学稳定性和组成等所需性质的新晶体结构.
- 解决结晶结构生成的挑战,包括晶格和原子位置的确定.
主要方法:
- 适应基于分数的概率模型,使用冷却的朗格温动力学来生成晶体.
- 引入了一种新的方法,在这种方法中,结晶格子是学习的,并与原子位置一起生成.
- 使用了尊重对称性约束的多图形晶体表示.
主要成果:
- 该模型成功地在各种化学系统和晶体组之间生成新的候选晶体结构,而无需重新训练.
- 多图形表示提供了计算优势,并提高了生成结构的质量.
- 通过对现有生成模型进行比较分析来证明模型的能力.
结论:
- 拟议的机器学习模型提供了一种强大而灵活的方法,用于 de novo 晶体结构的生成.
- 这种方法通过克服传统的局限性,推进了具有定制性质的新材料的设计.
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