发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出NNLap，一种基于精确交换关联势与能量训练的拉普拉斯级泛函，通过神经网络修正PBE，在热化学基准上达到与SCAN和r2SCAN相当的精度，且无需轨道依赖。
AI 中文摘要
我们提出了NNLap，一种机器学习拉普拉斯级交换关联（XC）泛函，它通过一个依赖于电子密度、其梯度和拉普拉斯量的神经网络修正来增强PBE。该模型在通过逆密度泛函理论（DFT）计算配置相互作用密度获得的精确XC势和能量上进行训练。尽管仅训练于少数几个系统——五个原子和三个分子——该模型在热化学基准上取得了显著精度，与meta-GGA泛函SCAN和r2SCAN相当。它还获得了准确的总能量，与SCAN相当，优于r2SCAN和B3LYP。这表明，在精确XC势和能量上训练的拉普拉斯级模型可以达到meta-GGAs的精度，而无需其轨道依赖性。
英文摘要
We present NNLap, a machine-learned Laplacian-level exchange-correlation (XC) functional that augments PBE with a neural-network correction depending on the electron density, its gradient, and its Laplacian. The model is trained on exact XC potentials and energies, obtained through inverse density-functional theory (DFT) calculations on configuration-interaction densities. Despite training on only a few systems -- five atoms and three molecules -- the model achieves remarkable accuracy on thermochemistry benchmarks, competing with the meta-GGA functionals SCAN and r2SCAN. It also attains accurate total energies, comparable to SCAN and better than r2SCAN and B3LYP. This shows that a Laplacian-level model, trained on exact XC potentials and energies, can reach the accuracy of meta-GGAs without their orbital dependence.
