activity
20172023
most citedCollective dynamics in atomistic models with coupled translational and spin degrees of freedom

34 citations · 60 across the 13 of their papers we have counts for

collaborators
Showing cond-mat.mtrl-sciShow all

11 papers · 1 filter

cond-mat.mtrl-sci2023

First principles residual resistivity using locally self-consistent multiple scattering method

Vishnu Raghuraman, Markus Eisenbach, Michael Widom +1

The locally self-consistent multiple scattering (LSMS) method can perform efficient first-principles calculations of systems with large number of atoms. In this work, we combine th…

cond-mat.mtrl-sci2022

Order Parameter Engineering for Random Systems

G. Anand, Swarnava Ghosh, Markus Eisenbach

The chemical short-range order (CSRO) in the crystalline materials influences the properties and its effect is particularly important in the context of the multicomponent materials…

cond-mat.mtrl-sci20202 cited

Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach

Xianglin Liu, Jiaxin Zhang, Junqi Yin +3

High entropy alloys (HEAs) are a series of novel materials that demonstrate many exceptional mechanical properties. To understand the origin of these attractive properties, it is i…

cond-mat.mtrl-sci20202 cited

Tuning Fermi Levels in Intrinsic Antiferromagnetic Topological Insulators MnBi2Te4 and MnBi4Te7 by Defect Engineering and Chemical Doping

Mao-Hua Du, Jiaqiang Yan, Valentino R. Cooper +1

MnBi2Te4 and MnBi4Te7 are intrinsic antiferromagnetic topological insulators, offering a promising materials platform for realizing exotic topological quantum states. However, high…

cond-mat.mtrl-sci2020

Predicting the phase stability of multi-component high entropy compounds

Krishna Chaitanya Pitike, Santosh KC, Markus Eisenbach +2

A generic method to estimate the relative feasibility of formation of high entropy compounds in a single phase, directly from first principles, is developed. As a first step, the r…

cond-mat.mtrl-sci2019

Machine Learning the Effective Hamiltonian in High Entropy Alloys

Xianglin Liu, Jiaxin Zhang, Markus Eisenbach +1

The development of machine learning sheds new light on the problem of statistical thermodynamics in multicomponent alloys. However, a data-driven approach to construct the effectiv…