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
DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science
In this paper. the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 69 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 300 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 743 entries, the paper represents a snapshot of DFT, anno 2022.
more »
« less
- Award ID(s):
- 1939528
- PAR ID:
- 10326296
- Date Published:
- Journal Name:
- Physical chemistry chemical physics
- ISSN:
- 1463-9076
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
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-based
Δ -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. -
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
-
Gwizdka, Jacek ; Rieh, Soo Young (Ed.)Keeping up with the research literature plays an important role in the workflow of scientists – allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a deluge of research and coordinated efforts to develop AI tools intended to uncover the research frontier. Despite these trends indicative of the information overload faced by data scientists, no prior work has examined the specific practices and challenges faced by these scientists in an interdisciplinary field with evolving scholarly norms. In this paper, we close this gap through a set of semi-structured interviews and think-aloud protocols of industry and academic data scientists (N = 20). Our results while corroborating other knowledge workers’ practices uncover several novel findings: individuals (1) are challenged in seeking and sensemaking of papers beyond their disciplinary bubbles, (2) struggle to understand papers in the face of missing details and mathematical content, (3) grapple with the deluge by leveraging the knowledge context in code, blogs, and talks, and (4) lean on their peers online and in-person. Furthermore, we outline future directions likely to help data scientists cope with the burgeoning research literature.more » « less
-
null (Ed.)We previously demonstrated that Milstein's seminal diethylamino-substituted PNN-pincer–ruthenium catalyst for ester hydrogenation is activated by dehydroalkylation of the pincer ligand, releasing ethane and eventually forming an NHEt-substituted derivative that we proposed is the active catalyst. In this paper, we present a computational and experimental mechanistic study supporting this hypothesis. Our DFT analysis shows that the minimum-energy pathways for hydrogen activation, ester hydrogenolysis, and aldehyde hydrogenation rely on the key involvement of the nascent N–H group. We have isolated and crystallographically characterized two catalytic intermediates, a ruthenium dihydride and a ruthenium hydridoalkoxide, the latter of which is the catalyst resting state. A detailed kinetic study shows that catalytic ester hydrogenation is first-order in ruthenium and hydrogen, shows saturation behavior in ester, and is inhibited by the product alcohol. A global fit of the kinetic data to a simplified model incorporating the hydridoalkoxide and dihydride intermediates and three kinetically relevant transition states showed excellent agreement with the results from DFT.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
