使用Hartree-Fock计算数据和机器学习模型预测HOMO-LUMO差距
Md Mehedi Hasan1, Omid Tarkhaneh2, Sharene D Bungay2
1Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
Journal of chemical information and modeling
|September 10, 2025
概括
机器学习模型快速预测最高占有分子轨道-最低未占有分子轨道 (HOMO-LUMO) 差距,克服计算和实验挑战. 一个整体模型实现了高精度,识别了各种应用的关键分子描述符.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习应用 机器学习应用
背景情况:
- 量子力学 (QM) 对最高占用分子轨道-最低不占用分子轨道 (HOMO-LUMO) 差距的计算非常昂贵.
- 对HOMO-LUMO差距的实验性确定是耗时且昂贵的.
- 机器学习 (ML) 为预测这些电子性质提供了一个具有成本效益和快速的替代方案.
研究的目的:
- 开发和评估ML模型来预测有机分子中的HOMO-LUMO能量差距.
- 探索从简化分子输入线输入系统 (SMILES) 获得的分子描述符的使用,用于ML模型训练.
- 创建一个集体ML模型,以提高预测准确性和适用于多种分子结构的应用性.
主要方法:
- 使用了46,717个小分子的数据集,其中HOMO-LUMO差距值来自Hartree-Fock (HF) 计算.
- 使用RDKit从SMILES表示生成的分子描述符.
- 训练并比较了各种基于回归的ML模型,包括LightGBM,双向LSTM,CatBoost和多层感知器 (MLP).
主要成果:
- 轻GBM,双向LSTM,CatBoost和MLP模型实现了0.25 eV以下的平均绝对误差 (MAE).
- 结合LightGBM,双向LSTM和MLP的加权组合模型实现了0.1660 eV的MAE.
- SHAP分析确定了20个关键的分子描述符,模型被调整为实验性HOMO-LUMO差距估计,直到碳号50.
结论:
- 机器学习模型,特别是集体方法,可以高效准确地预测HOMO-LUMO差距.
- 开发的模型证明了对小分子和大分子的多功能性,有助于高通量选.
- 该研究强调了机器学习在通过减少对密集计算和实验的依赖来加速化学发现方面的潜力.
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