局部敏感的基于哈希的数据集减少,用于深度潜力训练
Anmol1, Anuj Kumar Sirohi2, Neha1
1Department of Chemistry, Indian Institute of Technology, Delhi, Hauz Khas, New Delhi 110016, India.
Journal of chemical theory and computation
|June 10, 2025
概括
这项研究引入了一种新的局部敏感散列方法,以显著减少训练机器学习潜力所需的数据,降低量子化学计算的成本. 这种方法可以准确计算化学反应和相变的自由能量.
科学领域:
- 计算化学的计算化学
- 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 (ML) 潜力需要从昂贵的初始计算中获得广泛的数据以获得准确性.
- 目前的方法在平衡数据集大小,多样性和计算成本方面面临挑战.
研究的目的:
- 开发一种新的方法来减少对ML潜力的培训数据集的大小.
- 为了降低初始计算的计算成本,同时保持数据质量和多样性.
- 为复杂系统提供准确的自由能量计算.
主要方法:
- 实施了局部敏感哈希 (LSH) 方法来选择多样化和准确的数据点.
- 使用减少的数据集开发了ML潜力.
- 用ML潜能进行了温和的元动力学模拟.
主要成果:
- 实现了近一个数量级的数据集大小缩小.
- 成功开发了化学反应和相变的ML潜力.
- 计算了两个系统的收的自由能量表面.
结论:
- 基于LSH的方法有效地减少了对ML潜力的数据要求.
- 这种方法大大降低了开发准确的ML潜力的成本.
- 能够对化学和材料过程进行高效的自由能量计算.
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