用机器学习对密度函数的系统特异性重组化:应用于过渡金属复合体的自旋分裂能量
Aaron G Garrison1, Heather J Kulik1,2
1Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
通过将系统特定的Hartree-Fock交换 (HFX) 添加到密度函数来改进过渡金属复合体 (TMC) 的精确旋转分裂能量 (SSE) 计算. 机器学习模型预测最佳的HFX,减少错误,以获得更好的催化剂和材料设计.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 量子力学就是量子力学.
背景情况:
- 精确的旋转分裂能量 (SSE) 估计对于过渡金属复合物 (TMC) 催化剂和材料至关重要.
- 目前的方法在SSE中难以达到高精度,限制了可靠的建模.
- 密度函数理论 (DFT) 通常需要系统特定的调以获得最佳性能.
研究的目的:
- 开发一种可靠的方法,用于在TMC中准确的SSE计算.
- 研究将Hartree-Fock交换 (HFX) 添加到半语言函数的有效性.
- 建立一种机器学习 (ML) 方法,以系统依赖的方式预测最佳的HFX.
主要方法:
- 将Hartree-Fock交换 (HFX) 添加到半局部密度函数 (例如,PBE,SCAN) 的系统特定添加.
- 对450多个TMC的高级波函数理论 (DLPNO-CCSD(T)) 基准进行评估.
- 在电子密度上训练的Behler-Parrinello神经网络的开发,以预测最佳的HFX数量.
主要成果:
- 与参考DLPNO-CCSD (T) 值相比,添加特定系统的HFX可以显著减少错误.
- 全球重定量化,电离电位调整和基于原子属性的ML模型是不够的.
- 在未见的数据上,ML方法实现了<5%的HFX误差 (<3-4kcal/mol),与最先进的函数相竞争.
结论:
- 一个基于电子密度的ML衍生自调功能提供了一个实用和可解释的通往准确的SSE的路径.
- 这种方法可以更可靠,更有效地选TMC应用中的化学空间.
- 该方法提高了密度函数的准确性,用于关键材料科学和催化问题.
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