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1. DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science

   A.M. Teale, T. Helgaker (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 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. 

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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. 

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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. 

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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. 

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5. How Data Scientists Review the Scholarly Literature

   https://doi.org/10.1145/3576840  

   Mysore, Sheshera; Jasim, Mahmood; Song, Haoru; Akbar, Sarah; Randall, Andre Kenneth; Mahyar, Narges (March 2023, ACM)

   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. 

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