用信息理论方法预测农衍生物的氧化潜在和哈梅特参数
Mingxin Xu1, Yilin Zhao2, Hui Li3
1School of Pharmaceutical Sciences and Yunnan Key Laboratory of Pharmacology for Natural Products, Kunming Medical University, Kunming 650500, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
预测化学性质,例如氧化还原潜力,是一项挑战. 使用信息理论方法 (ITA) 的新量子机器学习 (QML) 和深度学习 (DL) 方法显示出有前途的数量,QML的表现优于DL.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 机器学习在化学中的应用
背景情况:
- 准确地预测氧化还原潜力和哈梅特常数在化学中至关重要.
- 现有的基于密度的函数来自信息理论方法 (ITA) 量,在效率和准确性方面面临挑战.
- 昆衍生物是研究电子性质的重要模型系统.
研究的目的:
- 基于ITA量,评估深度学习 (DL) 和量子机器学习 (QML) 协议的有效性,用于预测氧化还原潜力和哈梅特常量.
- 评估这些模型对各种化学系统的可转移性.
- 为了比较DL(ITA) 和QML(ITA) 方法的性能.
主要方法:
- 使用了两个协议:DL(ITA) 和QML(ITA).
- 在酸性原子的核中使用的分子静电电位 (MEP) 和价值自然原子轨道 (NAO) 能量的总和.
- 将线性回归 (LR) 框架应用于具有实验数据的多种类型的子衍生物.
主要成果:
- 在DL ((ITA) 协议中,开发了一个可转移的模型,适用于和非系统.
- 与DL (ITA) 方法相比,QML (ITA) 模型表现出优异的预测性能.
- 这两种方法都在LR框架内使用ITA量来进行财产预测.
结论:
- 与DL(ITA) 相比,QML(ITA) 提供了一个更准确,更有效的方法来预测与DL(ITA) 相比的氧化还原潜力和哈梅特常数.
- 该QML(ITA) 方法显示了近期在量子硬件上实施的潜力.
- 这项工作推动了机器学习和信息理论在计算化学中的应用.
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