基于内核的量子机器学习以创纪录的速度:多体分布函数作为紧的表示形式
Danish Khan1,2, Stefan Heinen2, O Anatole von Lilienfeld1,2,3,4
1Department of Chemistry, University of Toronto, St. George Campus, Toronto, Ontario M5S 1A1, Canada.
The Journal of chemical physics
|July 18, 2023
概括
基于原子高斯多体分布函数 (MBDF) 的新量子机器学习 (QML) 表示,提供了准确和高效的化学系统建模. 这些紧的MBDF模型降低了培训和使用QML的计算成本,加速了化学发现.
科学领域:
- 计算化学是一种计算化学.
- 量子机器学习就是量子机器学习.
- 材料科学是一种材料科学.
背景情况:
- 基于内核的量子机器学习 (QML) 模型证明了化学系统的高数据效率.
- 准确的化学表征通常需要高维特征映射,导致显著的计算负担.
- 有效和准确的分子表示对于在化学化合物空间中推进QML至关重要.
研究的目的:
- 引入新的,紧的,准确的线性缩放量子机器学习表示.
- 开发对原子数不变的表示,减少计算复杂性.
- 根据最先进的方法来评估这些新表示的性能和数据效率.
主要方法:
- 使用原子高斯多体分布函数 (MBDF) 和它们的衍生品开发紧的,线性缩放的QML表示.
- 使用MBDF值的加权密度函数作为全局,固定大小表示.
- 测试QM9和QMugs数据集上的各种分子属性的表示.
主要成果:
- 提出的基于MBDF的表示实现了预测性能和训练数据效率,与最先进的方法相竞争.
- 这些表示表明了各种分子性质的概括能力,包括能量,电子性质和热力学参数.
- 基于MBDF的模型显著改善了采样和培训成本之间的权衡,使化学空间的探索速度更快.
结论:
- 基于MBDF的集成QML表示为化学系统建模提供了计算效率高,准确的替代方案.
- 这些表示方便快速准确地取样化学化合物空间,加速材料的发现.
- 开发的方法提供了一种途径,以显著降低计算成本实现化学精度.
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