View in EDS

An Energy‐Corrected Fast Post‐SCF Local‐Hybrid Scheme for Highly Accurate Energy Differences of Large Main‐Group Systems.

Saved in:
Bibliographic Details
Title: An Energy‐Corrected Fast Post‐SCF Local‐Hybrid Scheme for Highly Accurate Energy Differences of Large Main‐Group Systems.
Authors: Wodyński, Artur1 (AUTHOR) artur.wodynski@tu‐berlin.de, Kaupp, Martin1 (AUTHOR) martin.kaupp@tu‐berlin.de
Source: Journal of Computational Chemistry. 6/30/2026, Vol. 47 Issue 17, p1-11. 11p.
Subjects: Computational chemistry, Density functionals
Abstract: Local‐hybrid (LH) density functionals admix exact exchange locally in real space and thereby can be powerful tools to ameliorate the usual zero‐sum game between reducing self‐interaction errors and modeling static correlation. But as with other hybrid functionals the practical use of LHs for large systems is limited by the cost of evaluating exact‐exchange quantities self‐consistently. Here we introduce an energy‐corrected local‐hybrid framework, EC(LH)@(m)GGA, in which a computationally expedient semi‐local reference density is used for a single post‐SCF evaluation with an advanced LH. Using the recent neural‐network‐based LH25nP LH as a prototype, we show that the EC route based on GGA or meta‐GGA orbitals preserves the characteristic accuracy profile of the parent local hybrid and reaches state‐of‐the‐art rung 4 performance on the GMTKN55 test suite (WTMAD‐2 around 2.4–2.7 kcal/mol, depending on grid). The top performance of LH25nP for spin‐restricted bond dissociation as a strong‐correlation measure is retained in this framework. The dominant post‐SCF overhead in timing is governed by the EC grid. For the practical gridsize 3, the total EC(LH)@(m)GGA cost is only about ~2–3× that of a GGA single point, typically about an order of magnitude less than a full LH SCF. Overall, EC(LH)@(m)GGA provides a simple post‐SCF route to state‐of‐the‐art rung‐4 energetics at a cost close to semi‐local DFT, applicable to large systems. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Local‐hybrid (LH) density functionals admix exact exchange locally in real space and thereby can be powerful tools to ameliorate the usual zero‐sum game between reducing self‐interaction errors and modeling static correlation. But as with other hybrid functionals the practical use of LHs for large systems is limited by the cost of evaluating exact‐exchange quantities self‐consistently. Here we introduce an energy‐corrected local‐hybrid framework, EC(LH)@(m)GGA, in which a computationally expedient semi‐local reference density is used for a single post‐SCF evaluation with an advanced LH. Using the recent neural‐network‐based LH25nP LH as a prototype, we show that the EC route based on GGA or meta‐GGA orbitals preserves the characteristic accuracy profile of the parent local hybrid and reaches state‐of‐the‐art rung 4 performance on the GMTKN55 test suite (WTMAD‐2 around 2.4–2.7 kcal/mol, depending on grid). The top performance of LH25nP for spin‐restricted bond dissociation as a strong‐correlation measure is retained in this framework. The dominant post‐SCF overhead in timing is governed by the EC grid. For the practical gridsize 3, the total EC(LH)@(m)GGA cost is only about ~2–3× that of a GGA single point, typically about an order of magnitude less than a full LH SCF. Overall, EC(LH)@(m)GGA provides a simple post‐SCF route to state‐of‐the‐art rung‐4 energetics at a cost close to semi‐local DFT, applicable to large systems. [ABSTRACT FROM AUTHOR]
ISSN:01928651
DOI:10.1002/jcc.70431