最佳位置依赖的数据驱动学习精确交换能量密度混合以改善密度功能
Martin Kaupp1, Nóra Kovács1, Artur Wodyński1
1Institut für Chemie, Theoretische Chemie/Quantenchemie, Technische Universität Berlin, Sekr. C7, Straße des 17. Juni 135, D-10623 Berlin, Germany.
The journal of physical chemistry. A
|December 31, 2025
概括
本研究介绍了一种基于数据的方法,使用神经网络来增强密度函数,以提高准确性. 该方法通过学习位置依赖的精确交换添加剂来改进局部混合函数,从而实现更好的预测和减少文物.
科学领域:
- 量子化学 是一个量子化学.
- 计算材料科学科学 计算材料科学
- 机器学习在化学中的应用
背景情况:
- 大致密度函数对材料科学至关重要,但在价值区域中难以准确.
- 现有的方法面临的挑战是尺寸模糊性和缺乏确切的约束.
- 手工制作的不均性措施限制了局部混合功能器的性能.
研究的目的:
- 开发一个透明的,数据驱动的路线来改进近似密度函数.
- 用神经网络局部混合函数 (n-LMFs) 取代传统的不均质度.
- 为了提高本地混合动力和双混合动力功能的准确性和可靠性.
主要方法:
- 使用的神经网络局部混合函数 (n-LMFs) 评估在阶段-3或阶段-4描述符上.
- 保持了整体的功能结构透明和可解释.
- 将方法扩展到五级函数,并结合了SCS-PT2相关性.
- 训练了一个具有明确强相关系因子的n-LMF来解决移位和静态相关系错误.
主要成果:
- 开发了具有广泛主组精度的LH24n-B95和LH24n函数.
- 成功地抑制了没有校准功能的测量器件,允许实时空间分析.
- 引入了第一个局部双杂交,具有取决于位置的精确交换添加剂和SCS-PT2相关性.
- 该LH25nP功能实现了最先进的4级性能,改善了键解离曲线,分数旋转行为,并减少了旋转污染.
结论:
- 有限的机器学习集成保留了可解释性,并促进了合理的设计.
- 开发的功能性为化学应用提供了显著的准确性和稳定性的改进.
- 这种数据驱动的方法为推进密度函数理论提供了一个强大的工具.
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