arXiv subjects

Kanun Pokharel

Publications and source records attributed to Kanun Pokharel.

4 recordsLinked to original sources

arXiv2026

Rationalizing defect formation energies in metals and semiconductors with semilocal density functionals

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 systems: monovacancies in eight fcc metals, and interstitials in the semiconductor Si-diamond. Specifically, we compute defect formation energies using the local density approximation, the Perdew-Burke-Ernzerhof generalized gradient approximation, the meta-generalized gradient approximations (meta-GGAs) strongly constrained and appropriately normed (SCAN), its regularized version (r2SCAN), the Lebeda-Aschebrock-Kummel (LAK) meta-GGA, and the Heyd-Scuseria-Ernzerhof screened hybrid functional. For metals, the local density approximation shows better performance compared to the other approximations, whereas for silicon, the meta-generalized gradient approximation Lebeda-Aschebrock-Kummel yields outstand- ing accuracy, surpassing the hybrid functional and approaching the results of more computationally demanding Quantum Monte Carlo methods. To rationalize the different performances, we study the semilocal ingredients rs, s and α in both the pristine and defective structures. We identify critical regions that indicate the observed trends of the defect formation energies and pave the way for improving density functional approximations.

cond-mat.mtrl-sci↗

arXiv2025

Advances and challenges of SCAN and r2SCAN density functionals in transition-metal compounds

Transition-metal compounds (TMCs) with open-shell d-electrons are characterized by a complex interplay of lattice, charge, orbital, and spin degrees of freedom, giving rise to a diverse range of fascinating applications. Often exhibiting exotic properties, these compounds are commonly classified as correlated systems due to strong inter-electronic interactions called Hubbard U. This inherent complexity presents significant challenges to Kohn-Sham density functional theory (KS-DFT), the most widely used electronic structure method in condensed matter physics and materials science. While KS-DFT is, in principle, exact for the ground-state total energy, its exchange-correlation energy must be approximated in practice. The mean-field nature of KS implementations, combined with the limitations of current exchange-correlation density functional approximations, has led to the perception that DFT is inadequate for correlated systems, particularly TMCs. Consequently, a common workaround involves augmenting DFT with an on-site Hubbard-like U correction. In recent years, the strongly constrained and appropriately normed (SCAN) density functional, along with its refined variant r2SCAN, has achieved remarkable progress in accurately describing the structural, energetic, electronic, magnetic, and vibrational properties of TMCs, challenging the traditional perception of DFT's limitations. This review explores the design principles of SCAN and r2SCAN, highlights their key advancements in studying TMCs, explains the mechanisms driving these improvements, and addresses the remaining challenges in this evolving field.

cond-mat.mtrl-sci↗

arXiv2022

Exact constraints and appropriate norms in machine learned exchange-correlation functionals

Machine learning techniques have received growing attention as an alternative strategy for developing general-purpose density functional approximations, augmenting the historically successful approach of human designed functionals derived to obey mathematical constraints known for the exact exchange-correlation functional. More recently efforts have been made to reconcile the two techniques, integrating machine learning and exact-constraint satisfaction. We continue this integrated approach, designing a deep neural network that exploits the exact constraint and appropriate norm philosophy to deorbitalize the strongly constrained and appropriately normed SCAN functional. The deep neural network is trained to replicate the SCAN functional from only electron density and local derivative information, avoiding use of the orbital dependent kinetic energy density. The performance and transferability of the machine learned functional are demonstrated for molecular and periodic systems.

physics.chem-ph↗

arXiv2022

Sensitivity of the electronic and magnetic structures of cuprate superconductors to density functional approximations

We discuss the crystal, electronic, and magnetic structures of $\mathrm{La_{2-x}Sr_{x}CuO_{4}}$ (LSCO) for $x=0.0$ and $x=0.25$ employing 13 density functional approximations, representing the local, semi-local, and hybrid exchange-correlation approximations within the Perdew-Schmidt hierarchy. The meta-generalized gradient approximation (meta-GGA) class of functionals is found to perform well in capturing the key properties of LSCO, a prototypical high-temperature cuprate superconductor. In contrast, the local-spin-density approximation, GGA, and the hybrid density functional fail to capture the metal-insulator transition under doping.

cond-mat.supr-con↗