关于在机器学习的集体变量空间中自由能量表面的可重现性
Florian M Dietrich1, Matteo Salvalaglio1
1Thomas Young Centre and Department of Chemical Engineering, University College London, London WC1E 7JE, United Kingdom.
The Journal of chemical physics
|October 13, 2025
概括
使用机器学习集体变量 (MLCVs) 的分子模拟中的可重现性具有挑战性. 我们提出了几何自由能量表示和正常化因子,以确保在不同培训实例和架构中得到一致的结果.
科学领域:
- 计算化学是一种计算化学.
- 分子动力学模拟的模拟.
- 在科学领域的机器学习.
背景情况:
- 机器学习集体变量 (MLCV) 越来越多地用于分子模拟.
- 由于训练的可变性和超参数空间复杂性,存在可重复性挑战.
- 目前用于用MLCVs表示自由能量表面的方法缺乏一致性.
研究的目的:
- 解决利用MLCVs计算和表示自由能量表面的可重现性危机.
- 提出新的方法来提高基于MLCV的模拟的一致性和可靠性.
- 为了促进MLCV在计算化学中的更广泛的采用和部署.
主要方法:
- 采用几何 (尺度不变) 自由能量表示.
- 在偏差增强采样中引入模型梯度的正常化因子.
- 分析训练过程的可变性和超参数空间特征.
主要成果:
- 几何自由能量表示确保在不同的培训实例和架构中保持一致的自由能量差异.
- 规范化因子统一了用于增强采样的自由能量定义.
- 提出的方法减轻了阻碍MLCV部署的实际问题.
结论:
- 建议的几何表示和规范化因子对于可重复的MLCV计算至关重要.
- 这些进展为在分子模拟中更可靠,更广泛地使用MLCV铺平了道路.
- 解决可重现性是释放计算机科学中机器学习全部潜力的关键.
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