来自机器学习随机森林回归算法的分子黑森矩阵
Giorgio Domenichini1, Christoph Dellago1
1Faculty of Physics, University of Vienna, Kolingasse 14-16, 1090 Vienna, Austria.
这项研究引入了一种使用随机森林的机器学习模型,以快速估计分子Hessians. 这种方法可以准确地预测分子振动频率和能量.
科学领域:
- 计算化学是一种计算化学.
- 在量子化学中的机器学习.
背景情况:
- 计算分子赫西安是计算密集的.
- 准确的hessian对于理解分子振动和能量至关重要.
研究的目的:
- 开发一个快速而准确的机器学习模型,用于分子赫西安估计.
- 为了能够有效地预测振动频率,正常模式和零点能量.
主要方法:
- 采用了一个基于森林的随机机器学习模型.
- 该模型学习了与内部坐标相对应的能量的第二导数.
- 旋转和转换不变性通过坐标表示来确保.
主要成果:
- 该模型提供了快速而准确的赫森估计.
- 它是在QM7数据上进行训练,并在QM9分子上进行验证.
- 实现了对振动频率,正常模式和ZPE的合理预测.
结论:
- 机器学习为黑斯计算提供了一个有效的替代方案.
- 开发的模型显示了对更大的分子系统的前景.
- 这种方法可以加速计算化学工作流程.
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