基于原子定位嵌入式变压器模型用于预测晶体材料状态的密度
Yaning Cui1,2, Kang Chen3,4, Lingyao Zhang1,2
1Physics Department, Materials Genome Institute, International Center for Quantum and Molecular Structures, Shanghai University, Shanghai 200444, China.
The journal of physical chemistry letters
|August 30, 2023
概括
机器学习加速了材料的发现. 一个新的原子定位嵌入式变压器 (APET) 模型准确地预测了晶体材料的电子状态密度 (DOS),从而提高了对其特性的理解.
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:
- 机器学习 (ML) 正在改变科学发现,特别是在材料科学领域.
- 预测材料特性,特别是像电子状态密度 (DOS) 这样的光谱特性,对于理解晶体材料至关重要.
- 预测DOS的现有方法在准确性和完整性方面存在局限性.
研究的目的:
- 介绍一种新的ML模型,即基于原子定位嵌入的变压器 (APET),用于预测ab initio DOS.
- 为了更有效地利用结构信息来改进DOS预测.
- 提高材料科学中的ML模型的可解释性.
主要方法:
- 开发了APET,一种基于变压器的模型,利用原子位置信息作为嵌入式.
- 将晶体结构的全面结构数据纳入模型的位置编码.
- 评估APET与现有的最先进的DOS预测模型进行比较.
主要成果:
- 与当前领先的模型相比,APET在预测ab initio DOS方面表现优越.
- 原子定位嵌入有效地捕获了必要的结构信息,用于准确的预测.
- 该模型的可解释性允许更精确地识别潜在的材料特性.
结论:
- 通过机器学习,APET在预测状态的电子密度方面取得了重大进展.
- 该模型将结构信息纳入的方法为材料发现提供了更完整和更准确的表示.
- 通过APET的可解释性,可以更深入地了解晶体材料的物理性质.
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