基于同质性的持久描述器,用于无形结构的机器学习潜力
Emi Minamitani1,2,3, Ippei Obayashi3,4, Koji Shimizu5
1The Institute of Scientific and Industrial Research, Osaka University, Ibaraki 567-0047, Japan.
The Journal of chemical physics
|August 22, 2023
概括
我们介绍了一种使用持久同源 (PH) 机器学习潜力的新型描述器. 这种方法准确地预测无形材料的特性,为复杂的深度学习技术提供了更简单的替代方案.
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.
- 计算物理学的计算物理.
背景情况:
- 难以预测无形材料的特性.
- 机器学习潜力为计算密集的初始计算提供了替代方案.
- 对原子配置的有效描述器对于机器学习潜力至关重要.
研究的目的:
- 提出基于持久同质 (PH) 的机器学习潜力的新型描述符.
- 评估描述器预测无形材料物理性质的能力.
- 将描述符的特征与如图形神经网络 (GNN) 等现有方法进行比较.
主要方法:
- 使用持久性图 (PD),即PH的二维表示,来构建描述符.
- 来自PD的规范化2D直方图被用来表示原子配置.
- 分析了描述符空间的维度缩小,以了解它们的属性.
主要成果:
- 提出的描述器准确地预测了无形碳原子的平均能量.
- 即使使用简单的预测模型,描述器也表现良好.
- 尺寸还原分析显示,PH描述符与GNN隐性空间具有共同的特征.
结论:
- 持久的同源性为机器学习潜力的对称不变描述符的开发提供了一个有前途的方法.
- 这种方法绕过了对超参数调整和深度学习架构的需求.
- PH为材料属性预测提供了一个更简单但更有效的替代方案.
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