利用高旋转的DFT特征来预测3D过渡金属复合体中的旋转状态差距
Anuj Kumar Ray1, Sandeep Nagar2, Girish Varma2
1Indian Association for the Cultivation of Science, Kolkata, India. rcap@iacs.res.in.
Physical chemistry chemical physics : PCCP
|September 16, 2025
概括
本研究引入了一种机器学习 (ML) 模型,用于预测过渡金属复合体 (TMC) 的旋转状态能量差距 (SSE). 机器学习方法使用来自高旋转计算的描述符,为计算成本昂贵的方法提供了具有成本效益的替代方案.
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
- 量子化学 是一个量子化学.
背景情况:
- 在3D过渡金属复合体 (TMC) 中精确确定旋转状态能量差距 (SSE) 是至关重要的,但在计算上要求很高.
- 高级量子方法是可靠的,但对于大规模的TMC研究是不切实际的.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测 DFT 附带性 SSE 差距.
- 在SSE确定中绕过计算上昂贵的低旋转优化需求.
- 在过渡金属化学中创建一个成本效益高的SSE估计策略.
主要方法:
- 使用的描述符来源于单个高旋转DFT计算,并结合了晶体场理论知识.
- 描述器包括原子能水平,自然电荷,d轨道固有值和联体HOMO-LUMO差距.
- 在 934 个复合体中的 1434 个 SSE 值上训练 ML 模型.
主要成果:
- 在单牙测试组件上达到4.0kcalmol-1的最小平均绝对误差 (MAE).
- 已证明可转移到具有双酸 π 结合联体的复合体,可比MAE为6.6 kcal mol-1.1.
- 机器学习方法有效地预测了SSE,而不需要多参考低旋转优化.
结论:
- 开发的ML模型提供了一种具有成本效益和准确的方法,用于估计过渡金属复合体中的SSE.
- 这种方法为传统的计算方法提供了可行的替代方案,使大规模研究成为可能.
- 该研究有助于开发用于SSE预测的基于电子结构的新型描述器.
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