CASE21: Uniting Non-Empirical and Semi-Empirical Density Functional Approximation Strategies using Constraint-Based Regularization
CASE21: Uniting Non-Empirical and Semi-Empirical Density Functional Approximation Strategies using Constraint-Based Regularization
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CASE21：使用基于约束的正则化结合非经验和半经验密度函数逼近策略
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
R. DiStasio
中科院分区：
文献类型：
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作者：
Zachary M Sparrow;Brian G Ernst;Trine K Quady;R. DiStasio
在这项工作中，我们提出了一个通用的框架，统一了两个主要的策略，用于构建密度泛函近似（DFAs）：非经验（NE）约束满足和半经验（SE）数据驱动的优化。所提出的方法采用B样条-钟形样条函数与紧凑的支持-构建每个不均匀性校正因子（ICF）。这种选择通过使用Tikhonov正则化和惩罚B样条（P样条）来实现线性和非线性约束的显式强制以及ICF平滑，从而提供了多项式基础上的几个明显优势。作为概念证明，我们使用这种方法来构建CASE 21-一种约束和平滑的半经验混合广义梯度近似，完全满足PBE 0 NE-DFA所满足的所有约束（除了一个约束）（并部分满足剩余的一个约束），并在不同的化学性质集合中表现出增强的性能。因此，我们认为，本文提出的范例保持了NE-DFA的物理严格性和可转移性，同时利用高质量的量子力学数据来提高性能。
In this work, we present a general framework that unites the two primary strategies for constructing density functional approximations (DFAs): non-empirical (NE) constraint satisfaction and semi-empirical (SE) data-driven optimization. The proposed method employs B-splines -- bell-shaped spline functions with compact support -- to construct each inhomogeneity correction factor (ICF). This choice offers several distinct advantages over a polynomial basis by enabling explicit enforcement of linear and non-linear constraints as well as ICF smoothness using Tikhonov regularization and penalized B-splines (P-splines). As proof of concept, we use this approach to construct CASE21 -- a Constrained And Smoothed semi-Empirical hybrid generalized gradient approximation that completely satisfies all but one constraint (and partially satisfies the remaining one) met by the PBE0 NE-DFA and exhibits enhanced performance across a diverse set of chemical properties. As such, we argue that the paradigm presented herein maintains the physical rigor and transferability of NE-DFAs while leveraging high-quality quantum-mechanical data to improve performance.
影响因子：
5.5
作者：
Wheeler, Steven E.;Houk, K. N.
通讯作者：
Houk, K. N.
影响因子：
3.4
作者：
Lehtola, Susi;Steigemann, Conrad;Marques, Miguel A. L.
通讯作者：
Marques, Miguel A. L.
影响因子：
5.5
作者：
Parrish RM;Burns LA;Smith DGA;Simmonett AC;DePrince AE 3rd;Hohenstein EG;Bozkaya U;Sokolov AY;Di Remigio R;Richard RM;Gonthier JF;James AM;McAlexander HR;Kumar A;Saitow M;Wang X;Pritchard BP;Verma P;Schaefer HF 3rd;Patkowski K;King RA;Valeev EF;Evangelista FA;Turney JM;Crawford TD;Sherrill CD
通讯作者：
Sherrill CD