材料对称性识别和属性预测通过晶体囊表示完成
Chao Liang1, Yilimiranmu Rouzhahong1, Caiyuan Ye1
1School of Physics, Sun Yat-Sen University, Guangzhou, China.
Nature communications
|August 25, 2023
概括
本研究介绍了材料科学的对称性增强等价网络 (SEN). 这种新型机器学习模型通过学习晶体对称性和结构化学模式,准确地预测材料特性.
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:
- 准确预测材料特性需要了解全球晶体对称性和等价值信息.
- 现有的基于卷积网络的算法很难完全实现这种理解.
研究的目的:
- 开发一种机器学习 (ML) 模型,即对称性增强等差网络 (SEN),用于改进材料属性预测.
- 编码关节结构化学模式,并学习不同尺度的模式等价性.
主要方法:
- 开发了一种新的机器学习模型,称为对称性增强等差网络 (SEN).
- 利用囊变压器对晶体结构中的集群进行编码,并学习模式等差.
- 共同编码的结构化学模式用于材料表示.
主要成果:
- SEN准确地感知内在的晶体对称性和集群相互作用,减少有效的特征空间.
- 在MatBench数据集上,获得的平均绝对误差 (MAE) 为带隙0.181 eV,形成能量为0.0161 eV/原子.
- 证明感知对称性和相互作用对预测性能产生重大影响.
结论:
- SEN模型为设计材料科学ML模型提供了一种通用和可解释的方法.
- 隐式编码基于物理机制的特征关系可以提高预测准确性.
- SEN在机器学习领域取得了进展,用于预测材料属性.
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