基于基于物理的函数形式的初始分散潜力与机器学习
1Department of Chemistry, Virginia Commonwealth University, Richmond, Virginia 23284, USA.
The Journal of chemical physics
|May 8, 2024
概括
一个新的数据集,SAPT10K,提供9982个非共价相互作用能量来改进分散模型. 基于新特性的机器学习模型提高了各种分子复合物的准确性,优于现有方法.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 机器学习 机器学习
背景情况:
- 准确计算非共价相互作用对于理解分子行为至关重要.
- 像格里姆的D3和多体分散 (MBD) 等现有的分散模型在准确性和适用性方面存在局限性.
- 对称性适应扰动理论 (SAPT) 提供了准确的相互作用能量,但在计算上是昂贵的.
研究的目的:
- 介绍SAPT10K,这是一个关于非共价相互作用能量和组件的大数据集.
- 为分散相互作用开发改进的机器学习 (ML) 模型.
- 提高各种分子系统的分散模型的准确性和适用性.
主要方法:
- 使用SAPT2+(3)(CCD) 计算了9982个相互作用能量,与aug-cc-pVTZ基础集一起,形成SAPT10K数据集.
- 开发了基于ML模型的距离组图的新型分子间特征.
- 训练ML模型 (D3-ML和MBD-ML) 来预测分散能量,解决现有模型的缺陷.
主要成果:
- SAPT10K数据集涵盖了各种分子间复合体和潜在能量表面.
- 与CLIFF模型相比,开发的D3-ML模型仅使用笛卡尔坐标,在测试集上取得了更高的性能.
- ML模型证明了对广泛的元素和充电单体的适用性,超越了其他ML模型的局限性.
结论:
- SAPT10K为提炼初始分散潜力提供了有价值的数据.
- 新的ML模型为计算分散相互作用提供了一个计算效率高,准确的替代方案.
- 这些进步通过准确地建模非共价力,促进了对超分子组合和化学反应的研究.
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