A deconstructive analysis of charge-transfer and electrostatic field fluctuations to supplement first-principles modeling of disordered metals

ORAL

Abstract

High entropy alloys present a new class of disordered metals with hopeful applications in the next generation of materials and technology. However, much of the core physics underlying these and other, more generic forms of disordered matter remain the subject of ongoing inquiry. We thus present a minimal working model to describe random fluctuations in electronic charge and electrostatic "Madelung" field configurations in disordered metals. This work reveals both the nature and microscopic origins of these statistical qualities; it also suggests possible avenues for extending modern first-principles approaches to disorder that currently lack these features (e.g. conventional KKR-CPA). In our theory, disorder and interelectron Coulomb repulsion are incorporated in a standard perturbative manner, as is appropriate when simulating metallic alloys. The problem is then reformulated using a self-consistent linear response framework, which is capable of reproducing the same qualitative statistical trends obtained in more comprehensive treatments of disorder (e.g. LSMS) as we also show here. Our work may therefore bridge the gap between physical accuracy and computational affordability in first-principles disorder modeling and answer long-standing questions faced by the disordered materials community.

* The ab initio calculations in this work are based on open-source ab initio software package MuST, a project supported in part by NSF Office of Advanced Cyberinfrastructure and the Division of Materials Research within the NSF Directorate of Mathematical and Physical Sciences under Award Nos. 1931367 (HT), 1931445 (KT), and 1931525 (YW).

Presenters

  • Wai-Ga D Ho

    Florida State University

Authors

  • Wai-Ga D Ho

    Florida State University

  • Wasim R Mondal

    Middle Tennessee State University

  • Hanna Terletska

    Middle Tennessee State University

  • Ka Ming Tam

    Louisiana State University

  • Mariia Karabin

    Oak Ridge National Lab

  • Markus Eisenbach

    Oak Ridge National Laboratory

  • Yang Wang

    Pittsburgh Supercomput Ctr

  • Vladimir Dobrosavljevic

    Florida State University