基于机器学习的全球密度依赖范围分离参数的准确预测.
Corentin Villot1, Tong Huang1, Ka Un Lao1
1Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA.
The Journal of chemical physics
|July 24, 2023
概括
一个新的XGBoost模型准确地预测了长距离校正函数 (LRC-ωPBE) 的范围分离参数,绕过了复杂的计算. 这种机器学习方法可以对各种化学系统进行快速,高效的预测.
科学领域:
- 计算化学计算化学
- 机器学习在量子力学中的应用
- 密度函数理论 密度函数理论
背景情况:
- 准确预测电子特性需要精确的密度函数近似值.
- 长距离校正 (LRC) 函数,如LRC-ωPBE,提高准确性,但通常需要系统特定的参数调整.
- 确定最佳范围分离参数 (ω) 对LRC的功能性能至关重要.
研究的目的:
- 开发一个准确和高效的机器学习模型来预测LRC-ωPBE的全球密度依赖范围分离参数 (ωGDD).
- 仅使用原子坐标来实现快速,计算上便宜的 ωGDD 预测.
- 证明开发模型在预测分子性质和相互作用方面的实用性.
主要方法:
- 开发一个XGBoost机器学习模型 (ωGDDML) 使用本地原子环境的指纹和距离直方图.
- 该模型的培训和验证是在11466种不同的化学系统的大数据集上进行的.
- 应用LRC-ωPBE ((ωGDDML) 函数来预测极化性和非共价相互作用.
主要成果:
- ωGDDML模型在7046个复合体的测试集上实现了高精度,平均绝对误差为0.001117a0-1.
- 观察到很好的可转移性,只有0.07%的系统显示的错误大于0.01 a0-1.
- LRC-ωPBE ((ωGDDML) 在预测极化性方面表现出卓越的性能,并绕过了对非共价相互作用的传统初始系统特定调整的需求.
结论:
- 开发的 ωGDDML 模型提供了一个准确,高效和可转移的方法来确定 LRC-ωPBE 的范围分离参数.
- 这种数据驱动的方法通过消除对参数预测进行电子结构计算的需求,显著降低了计算成本.
- 物理启发的LRC-ωPBE与数据驱动的 ωGDDML模型的融合为量子力学计算提供了协同效益.
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