More Like this

1. DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science

   https://doi.org/10.1039/d2cp02827a  

   Teale, Andrew M.; Helgaker, Trygve; Savin, Andreas; Adamo, Carlo; Aradi, Bálint; Arbuznikov, Alexei V.; Ayers, Paul W.; Baerends, Evert Jan; Barone, Vincenzo; Calaminici, Patrizia; et al (August 2022, Physical Chemistry Chemical Physics)

   In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 302 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 777 entries, the paper represents a broad snapshot of DFT, anno 2022. 

   more » « less
2. Quantum chemical accuracy from density functional approximations via machine learning

   https://doi.org/10.1038/s41467-020-19093-1  

   Bogojeski, Mihail; Vogt-Maranto, Leslie; Tuckerman, Mark E.; Müller, Klaus-Robert; Burke, Kieron (October 2020, Nature Communications)

   Abstract Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol⁻¹with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol⁻¹) on test data. Moreover, density-basedΔ-learning (learning only the correction to a standard DFT calculation, termedΔ-DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness ofΔ-DFT  is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C₆H₄(OH)₂) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, thatΔ-DFT  facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. 

   more » « less
3. Numerical methods for Kohn–Sham density functional theory

   https://doi.org/10.1017/S0962492919000047  

   Lin, Lin; Lu, Jianfeng; Ying, Lexing (May 2019, Acta Numerica)

   Kohn–Sham density functional theory (DFT) is the most widely used electronic structure theory. Despite significant progress in the past few decades, the numerical solution of Kohn–Sham DFT problems remains challenging, especially for large-scale systems. In this paper we review the basics as well as state-of-the-art numerical methods, and focus on the unique numerical challenges of DFT. 

   more » « less
4. Understanding bonding of N-heterocyclic carbenes on Pd/Cu(111) single atom alloys via non-local density functional theory to store hydrogen via the molecular corking effect

   Simpson, S. (March 2024, ACS Spring 2024)

   N-heterocyclic carbenes(NHCs) have garnered the attention of material scientists and chemists for their tunable electronic properties. NHCs anchored to surfaces have attractive features and may provide new applications that traditional self-assembled monolayers (SAMs) have yet to be employed. In-fact, NHCs have been utilized to functionalize surfaces to tune reactivity and/or selectivity. However, the underlying mechanisms to control the surface-adsorbate interaction is still in its infancy, especially for SAAs. Herein we utilize periodic non-local density functional theory (DFT) calculations to better understand how changing the NHC backbone influences the bonding between the surface and the adsorbate with the end goal to utilize a relatively new mechanism to store hydrogen. 

   more » « less
5. Assessment of electron–proton correlation functionals for vibrational spectra of shared-proton systems by constrained nuclear-electronic orbital density functional theory

   https://doi.org/10.1063/5.0243086  

   Yang, Yuzhuo; Zhang, Yuzhe; Yang, Yang; Xu, Xi (December 2024, The Journal of Chemical Physics)

   Proton transfer plays a crucial role in various chemical and biological processes. A major theoretical challenge in simulating proton transfer arises from the quantum nature of the proton. The constrained nuclear-electronic orbital (CNEO) framework was recently developed to efficiently and accurately account for nuclear quantum effects, particularly quantum nuclear delocalization effects, in quantum chemistry calculations and molecular dynamics simulations. In this paper, we systematically investigate challenging proton transfer modes in a series of shared-proton systems using CNEO density functional theory (CNEO-DFT), focusing on evaluating existing electron–proton correlation functionals. Our results show that CNEO-DFT accurately describes proton transfer vibrational modes and significantly outperforms conventional DFT. The inclusion of the epc17-2 electron–proton correlation functional in CNEO-DFT produces similar performance to that without electron–proton correlations, while the epc17-1 functional yields less accurate results, comparable with conventional DFT. These findings hold true for both asymmetrical and symmetrical shared-proton systems. Therefore, until a more accurate electron–proton correlation functional is developed, we currently recommend performing vibrational spectrum calculations using CNEO-DFT without electron–proton correlation functionals. 

   more » « less