当代DFT:从传统和最近的趋势中学习,开发和评估准确的交换相关函数
E Brémond1, A J Pérez-Jiménez2, C Adamo3
1Université Paris Cité, ITODYS, CNRS, F-75006 Paris, France.
Physical chemistry chemical physics : PCCP
|January 28, 2026
概括
密度函数理论 (DFT) 的进步是由对交换相关函数的近似推进的. 新的深度学习模型补充了传统的功能模型,增强了电子结构计算.
科学领域:
- 计算物理 计算物理
- 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:
- 密度函数理论 (DFT) 是电子结构计算的基石.
- 对交换相关函数的准确近似对于DFT的实际应用至关重要.
- 超过50年的DFT研究已经为当前的进展奠定了基础.
研究的目的:
- 审查DFT的交换相关函数近似的历史发展和关键进展.
- 突出深度学习模型在DFT中的出现和影响.
- 讨论传统和新型功能发展之间的相互作用.
主要方法:
- 对DFT功能发展的历史分析.
- 对局部 (LDA),半局部 (GGA,元-GGA) 和非局部 (混合,双混合) 函数的审查.
- 在分子数据上训练的深度学习模型的检查.
主要成果:
- 在开发准确的交换相关函数方面取得了重大进展.
- 深度学习的函数 (例如,DM21,aPBE0-ML,Skala) 代表了一个新的前沿.
- 传统的函数仍然至关重要,预计将与机器学习方法共存.
结论:
- DFT 函数的演变标志着持续的创新,从传统的近似到复杂的深度学习模型.
- 开发大型,准确的数据集对于训练高级DFT功能人员至关重要.
- 未来的DFT应用程序将受益于各种各样的功能工具包,既有又新.
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