高质量的量子化学数据用于转化金属复合体中自旋状态的确定.
Mandira Dey1, Anuj Kumar Ray1, Vic Austen2
1School of Chemical Sciences, Indian Association for the Cultivation of Science, Kolkata, India. rcap@iacs.res.in.
Physical chemistry chemical physics : PCCP
|February 25, 2026
概括
机器学习因密度函数理论数据不可靠而与过渡金属旋转状态能量学扎. 这项研究引入了一个高精度数据集和一个新的描述符,以改善对这些具有挑战性的系统的机器学习预测.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 材料科学 材料科学 材料科学
背景情况:
- 机器学习 (ML) 模型在有机化学中表现出色,使用可靠的密度函数理论 (DFT) 数据.
- 对于过渡金属复合体,特别是自旋状态能量 (SSE),ML模型的可靠性较低,原因是DFT的系统依赖性不准确性.
研究的目的:
- 为了解决过渡金属SSE的ML中的数据限制.
- 创建一个基准数据集,并开发改进的ML模型,以准确的SSE预测.
主要方法:
- 计算了50个第一排单核八面体复合体的旋转能量差距,使用高级CASPT2/CC多引用方法.
- 系统地将各种DFT方法与高精度数据集进行了比较.
- 引入了基于电子结构的描述符 (Des-δ) 并使用了 Δ 机器学习 (Δ-ML) 框架.
主要成果:
- 证明了Hartree-Fock在DFT中的交换的最佳分数取决于特定的旋转状态过渡.
- 成功地将CASPT2/CC级准确度推断到500个复合体的更大集合中,使用D-ML框架与新的描述符.
结论:
- 开发的基准数据集和 Δ-ML 方法显著提高了 ML 对过渡金属 SSE 预测的可靠性.
- 这项工作为克服DFT在预测过渡金属复合物的SSE方面的局限性提供了一条途径.
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