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多性质电子的相关能量和分子的物理化学性质的深度学习

Yan Yuan^1,2,3, Yilin Zhao⁴, Linling Lu^1,2,3

¹Institute of Biomedical Research, Yunnan University, Kunming, Yunnan 650500, China.

Journal of chemical theory and computation

|June 10, 2025

概括

信息理论方法 (ITA) 描述符与深度学习 (DL) 结合,可以准确预测诸如关联能量和氧化还原潜力等分子性质. 这种多用途的ITA-DL协议显示了自动化化学研究的前景.

科学领域:

计算化学计算化学
机器学习在化学中的应用
量子化学是一个量子化学.

背景情况:

来自信息理论方法 (ITA) 的基于密度的描述符对于预测分子性质是有效的.
深度学习 (DL) 为多属性预测提供了一个强大的框架.
之前的工作证明了ITA在分子偏振性和NMR屏蔽方面的成功.

研究的目的:

应用ITA描述符和DL来预测更广泛的分子性质,包括电子相关性,氧化还原潜力,logKow和吸收波长 (λmax).
评估ITA-DL模型的稳定性,可转移性和通用性.
将ITA-DL模型的性能与TD-DFT等传统方法进行比较.

主要方法:

作为输入特征,使用了从信息理论方法 (ITA) 衍生出的基于密度的描述符.
采用多属性深度学习 (DL) 模型进行属性预测.
根据计算或实验数据进行验证的预测,包括对氧化还原潜的可转移性测试和对λmax的概括性测试.

主要成果:

ITA-DL模型在对应能量,氧化还原潜力,logKow和λmax的预测值和实际值之间取得了良好的一致性.
证明了对纳衍生物的氧化还原潜力预测的成功可转移性.
在比训练中使用的更大的分子上展示了对λmax预测的概括性,在准确性和效率方面超过了TD-DFT.

结论:

基于密度的多功能ITA量可以实现对广泛和密集分子性质的准确,低成本预测.
ITA-DL协议表现出稳健性和通用性,表明了闭环化学自动化的潜力.
这项工作支持基于ITA-DL模型进行化学性质预测的通用框架的开发.

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