张量减小的原子密度表示
James P Darby1,2, Dávid P Kovács2, Ilyes Batatia2,3
1Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom.
Physical review letters
|July 28, 2023
概括
新的张量缩小表示提供了紧的原子环境描述器. 这种方法避免了化学元素的扩展问题,使机器学习中的更广泛的数据分析和回归任务成为可能.
科学领域:
- 计算材料科学科学 计算材料科学
- 机器学习在化学和物理领域
- 数据分析和数据表示.
背景情况:
- 基于密度的原子环境表示对于原子模型中的机器学习至关重要.
- 目前使用元素特异密度的张量积的方法,随着元素数量的增加,其可扩展性较差.
- 图形神经网络通过学习嵌入提供了一个替代方案,但缺乏与基于密度的描述符的明确连接.
研究的目的:
- 开发一个紧的,元素数量独立的局部原子环境的表示.
- 通过张量分解将图形神经网络方法与传统的基于密度的描述符连接起来.
- 为各种数据驱动的原子化任务创建一个系统地可改进的表示.
主要方法:
- 重构图形神经网络原理作为邻近密度的基于描述符的张量分解.
- 开发一种新的符号,以识别与现有数据压缩算法的连接.
- 对原子环境进行张量减小表示的制定.
主要成果:
- 成功地获得了局部原子环境的紧张量缩小表示.
- 新表示的尺寸独立于所涉及的化学元素的数量.
- 该方法证明了系统的收性和适用于各种数据分析和回归任务的适用性.
结论:
- 拟议的缩小张量表示法克服了传统方法的可扩展性限制.
- 这种方法弥合了基于密度的描述符和图形神经网络之间的差距.
- 新的表示是用于材料科学和相关领域的机器学习的多功能工具.
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