可逆分子模拟用于训练经典和机器学习力场
1Medical Research Council Laboratory of Molecular Biology, Cambridge CB2 0QH, United Kingdom.
概括
下一代分子动力学力场将使用更多的数据. 我们开发了一种更快,更有效的微分模拟方法,使用实验数据准确地训练机器学习潜力.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 机器学习是机器学习.
背景情况:
- 开发精确的分子动力学力场对于模拟材料特性至关重要.
- 通过实验数据来训练机器学习潜力是具有挑战性的,因为计算成本.
- 微分分子模拟提供了一种途径,通过计算梯度来优化力场参数.
研究的目的:
- 改进微分分子模拟,以有效训练机器学习潜力.
- 开发一种反向时间模拟方法,减少内存和计算要求.
- 为了证明该方法在学习水,气体扩散和钻石潜力的有效性.
主要方法:
- 实现了反向时间模拟,以明确计算可观测物相对于力场参数的梯度.
- 实现了有效的恒定内存成本和与前向模拟可比的计算数量.
- 应用该方法来学习全原子水和气体扩散模型以及从头开始钻石机器学习潜力.
主要成果:
- 反向时间模拟方法显著提高了微分分子模拟中梯度计算的效率.
- 该方法成功地学习了水和气体扩散的准确模型,以及钻石潜力.
- 与组合重权衡的比较表明,可逆模拟产生了更准确的梯度.
结论:
- 改进的微分模拟方法提供了一种更有效,更准确的方式来训练机器学习潜力.
- 这一进步有助于开发下一代力量场,利用大型实验数据集.
- 该技术在计算化学和材料科学中广泛适用于优化分子模型.
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