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Recent investigations have found that the strongly constrained and appropriately normed (SCAN) meta-GGA exchange-correlation functional significantly over-magnetizes elemental Fe, Co, and Ni solids. For the paradigmatic case, bcc Fe, the…

Materials Science · Physics 2019-07-24 Daniel Mejia-Rodriguez , S. B. Trickey

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately…

We performed density functional calculations to estimate the formation energies of intermetallic alloys. We used two semilocal approximations, the generalized gradient approximation (GGA) by Perdew-Burke-Ernzerhof (PBE) and the strongly…

Materials Science · Physics 2020-11-25 Niraj K. Nepal , Santosh Adhikari , Bimal Neupane , Adrienn Ruzsinszky

We discuss self-consistently obtained ground-state electronic properties of monolayers of graphene and a number of beyond graphene compounds, including films of transition-metal dichalcogenides (TMDs), using the recently proposed strongly…

Materials Science · Physics 2017-04-27 I. G. Buda , C. Lane , B. Barbiellini , A. Ruzsinszky , J. Sun , A. Bansil

Modern advances in generating ultrabright electron beams have unlocked unprecedented experimental advances based on synchrotron radiation. Current challenges lie in improving the quality of electron sources with novel photocathode materials…

Materials Science · Physics 2024-02-27 Julia Santana-Andreo , Holger-Dietrich Saßnick , Caterina Cocchi

This study investigates the use of machine learning (ML) to correct the enthalpy of formation (Hf) from two separate DFT functionals, PBE and SCAN, to the experimental Hf across 1011 solid-state compounds. The ML model uses a set of 25…

Materials Science · Physics 2023-07-18 Santosh Adhikari , Christopher J. Bartel , Christopher Sutton

The development of novel materials for vacuum electron sources in particle accelerators is an active field of research that can greatly benefit from the results of \textit{ab initio} calculations for the characterization of the electronic…

Materials Science · Physics 2021-06-01 Holger-Dietrich Saßnick , Caterina Cocchi

We assess the accuracy and computational efficiency of the recently developed meta-generalized gradient approximation (metaGGA) functional, the restored regularized strongly constrained and appropriately normed (r$^2$SCAN), in transition…

Materials Science · Physics 2024-06-21 S. Swathilakshmi , Reshma Devi , Gopalakrishnan Sai Gautam

Layered lithium intercalating transition metal (TM) oxides are promising cathode materials for Li-ion batteries. Here we scrutinize the recently developed strongly constrained and appropriately normed (SCAN) density functional method to…

Materials Science · Physics 2018-12-31 Arup Chakraborty , Mudit Dixit , Dan T. Major

We propose modifications to the functional form of the SCAN density functional to eliminate numerical instabilities. This is necessary to allow reliable, automatic generation of pseudopotentials (including PAW potentials). The regularized…

Materials Science · Physics 2019-11-01 Albert P. Bartók , Jonathan R. Yates

Using the strongly constrained and appropriately normed (SCAN) and SCAN+U approximations for describing electron exchange-correlation (XC) within density functional theory, we investigate the oxidation energetics, lattice constants, and…

Materials Science · Physics 2018-09-27 Gopalakrishnan Sai Gautam , Emily A. Carter

We report a series of calculations testing the predictions of the Tran-Blaha functional for the electronic structure and magnetic properties of condensed systems. We find a general improvement in the properties of semiconducting and…

Materials Science · Physics 2010-11-08 David J. Singh

A systematic way of improving exchange-correlation energy functionals of density functional theory has been to make them satisfy more and more exact relations. Starting from the initial GGA functionals, this has culminated into the recently…

Chemical Physics · Physics 2017-10-11 Rabeet Singh , Manoj K. Harbola

The study of defects in materials is of utmost importance for technological applications and the design of new materials. In this work, we analyze the performance of density functional approximations on two prototypical sets of defective…

We combine a regularized variant of the strongly constrained and appropriately normed semilocal density functional [J. Sun, A. Ruzsinszky, and J. P. Perdew, Phys. Rev. Lett. 115, 036402 (2015)] with the latest generation semi-classical…

A meta generalized gradient level screened range-separated hybrid functional is developed for solid-state electronic structure theory. Assessment of the present range-separated hybrid functional for solid-state lattice constants and band…

Materials Science · Physics 2018-03-13 Subrata Jana , Abhilash Patra , Prasanjit Samal

We derive and motivate a Laplacian-level, orbital-free meta-generalized-gradient approximation (LL-MGGA) for the exchange-correlation energy, targeting accurate ground-state properties of $sp$ and $sd$ metallic condensed matter, in which…

Materials Science · Physics 2022-08-02 Aaron D. Kaplan , John P. Perdew

Accurate theoretical prediction of the band offsets at interfaces of semiconductor heterostructures can often be quite challenging. Although density functional theory has been reasonably successful to carry out such calculations and…

The performance of density functional theory (DFT) approximations for predicting materials thermodynamics is typically assessed by comparing calculated and experimentally determined enthalpies of formation from elemental phases, {\Delta}Hf.…

Materials Science · Physics 2019-01-08 Christopher J. Bartel , Alan W. Weimer , Stephan Lany , Charles B. Musgrave , Aaron M. Holder

Remarkable achievements have been attained by deep neural networks in various applications. However, the increasing depth and width of such models also lead to explosive growth in both storage and computation, which has restricted the…

Machine Learning · Computer Science 2019-06-11 Linfeng Zhang , Zhanhong Tan , Jiebo Song , Jingwei Chen , Chenglong Bao , Kaisheng Ma