一个展望标志着20年来,我们一直在用变量不变的多项式来计算分子潜力
Joel M Bowman1, Chen Qu2, Riccardo Conte3
1Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, USA.
The Journal of chemical physics
|May 13, 2025
概括
随机不变的多项式 (PIP) 对于开发精确的机器学习潜力 (MLP) 是必不可少的. 本视角回顾了自2018年以来的PIP进展,强调了它们在化学和材料科学中的广泛应用.
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 机器学习
背景情况:
- 在2004年引入了变不变的多项式 (PIP).
- PIP对于开发机器学习潜力 (MLP) 至关重要,因为它尊重潜在能量表面的基本对称性.
研究的目的:
- 提供PIP在2018年以来开发MLP中的进展和应用的全面概述.
- 突出PIP的优点,例如它们对原子排列的不变性和使用全局描述符.
主要方法:
- 关于PIP及其在MLP中的应用的文献审查.
- 讨论PIP在线性回归,神经网络和高斯过程回归中的使用.
- 专注于PIP在模拟化学反应,集群,凝结相和材料中的作用.
主要成果:
- 报告显示,在各种系统中,已有100多个利用PIP的潜力.
- PIP能够准确地建模复杂的能源景观,包括具有多个产品通道的化学反应.
- PIP已经成功地集成到各种机器学习框架中,以实现高准确度的预测.
结论:
- PIP是构建准确和高效的机器学习潜力的强大而通用的工具.
- 持续开发和应用PIP正在推进计算化学和材料科学领域.
- PIP提供了一种强大的方法,将基本的物理对称性纳入机器学习模型中.
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