一个简单的方法,以旋转不变的机器学习的矢量量量
Jakub Martinka1,2, Marek Pederzoli1, Mario Barbatti3,4
1J. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, v.v.i., Dolejškova 3, 18223 Prague 8, Czech Republic.
The Journal of chemical physics
|November 1, 2024
概括
一种新的旋转-预测-旋转 (RPR) 方法确保机器学习 (ML) 模型通过处理旋转不变性来准确预测分子向量属性,如二极子时刻. 这种方法简化了对分子动力学模拟的训练.
科学领域:
- 计算化学计算化学
- 机器学习在化学中的应用
- 分子动力学分子动力学
背景情况:
- 在机器学习 (ML) 中预测矢量和张量属性需要处理分子旋转不变,与能量等标量属性不同.
- 确保旋转共变的现有方法包括构建虚拟量或设计等同变量神经网络.
研究的目的:
- 引入一种更简单,更有效的方法,以在ML中预测矢量和张量属性的旋转共变度.
- 将旋转-预测-旋转 (RPR) 技术作为复杂等价ML模型的替代方案.
主要方法:
- 拟议的旋转-预测-旋转 (RPR) 方法涉及三个步骤:使用惯性张量将分子旋转到其主要轴上,在这个固定的框架中执行ML预测,并将预测反转.
- 实现使用了ML和分子动力学 (MD) 的MLatom和Newton-X程序.
- 该方法在1,2-二乙烯的MD轨迹期间评估了二极极时刻预测.
主要成果:
- RPR程序成功地保证了向量属性的正确共变性,通过准确的二极极 Moment 预测证明了这一点.
- 这种方法允许对众多分子配置进行准确的ML模型的快速训练,有利于主动学习.
结论:
- RPR技术提供了一种简单有效的方法,以确保分子向量和张量属性的ML预测的旋转共变性.
- 这种方法简化了ML模型的开发,用于诸如分子动力学等应用,其中旋转不变性至关重要.
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