基于人工神经网络的密度功能方法,用于过渡金属复合体中的亚亚巴特能差异
João Paulo Almeida de Mendonça1, Lorenzo Antonio Mariano1, Emilie Devijver2
1Université Grenoble Alpes, CNRS, Grenoble INP, SIMaP, 38000 Grenoble, France.
Journal of chemical theory and computation
|October 16, 2023
概括
一种新的机器学习方法增强了密度函数理论,用于预测过渡金属复合体中的自旋状态能量,提高了计算化学应用的准确性.
科学领域:
- 计算化学和物理计算化学和物理
- 量子化学是一种量子化学.
- 材料科学是一种材料科学.
背景情况:
- 大致的Kohn-Sham密度函数理论 (DFT) 被广泛使用,但与自旋状态能量学相斗争.
- 精确预测旋转状态对于理解过渡金属复杂行为至关重要.
研究的目的:
- 开发一种增强的交换相关函数,以更好地预测过渡金属复合体中的亚亚巴特能差异.
- 利用机器学习来纠正现有的DFT功能缺陷.
主要方法:
- 开发了一种使用人工神经网络校正的机器学习方法.
- 在电子密度,原子化能量和旋转状态能量学的一小部分数据集上训练了校正.
- 采用生物启发的粒子群集优化来训练功能.
主要成果:
- 机器学习的元泛化梯度近似 (mGGA) 函数显著优于现有的 DFT 函数.
- 在预测各种过渡金属复合体的电能差异方面表现出卓越的准确性.
- 展示了mGGA功能在各种协调环境和金属类型中的有效性.
结论:
- 开发的机器学习增强的DFT功能为准确的旋转状态能量预测提供了有希望的进步.
- 这种方法为涉及过渡金属复合物的计算研究提供了更可靠的工具.
- 强调了将机器学习与DFT集成到复杂化学问题的潜力.
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