以当地环境为指导的原子结构选择,以开发机器学习潜力
Renzhe Li1,2, Chuan Zhou1, Akksay Singh1,3,4
1Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, People's Republic of China.
The Journal of chemical physics
|February 21, 2024
概括
本研究介绍了一种新的算法,用于选择原子结构来训练机器学习潜力 (MLP). 该方法有效地将数据集大小减少80%,而不会牺牲MLP模型的性能.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 机器学习潜力 (MLP) 在计算化学和材料科学中非常重要,因为它们的准确性和效率.
- 开发可靠的MLP取决于选择适合训练的原子结构.
- 冗余或不足的数据阻碍了MLP的开发和性能.
研究的目的:
- 提出一个以当地环境为指导的选算法,用于在MLP开发中高效地选择数据集.
- 为了减少训练数据集的大小,同时保持MLP准确性.
- 提高MLP模型培训的稳定性和计算效率.
主要方法:
- 当地环境银行存储独特的原子局部环境.
- 使用欧几里德距离来评估不相似性,以确定新的环境.
- 只有当它们的当地环境与现有环境有显著差异时,才会选择新的结构.
- 该银行的更新是从选定的结构中更新新的本地环境.
主要成果:
- 该算法将Ge和Pd13H2系统的训练数据大小减少了约80%.
- 尽管数据集大小减少,但MLP模型的性能没有受到影响.
- 与最远点采样相比,该方法显示出更高的稳定性和计算效率.
- 结果独立于最初的结构选择和排序.
结论:
- 拟议的算法可以有效地选择数据集,以开发准确且计算效率高的MLP.
- 当地环境银行可以作为未来MLP开发的持续更新的数据库.
- 这种方法优化了训练数据选择过程,从而导致更好的MLP模型.
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