In‐Silico Thermodynamic Description of Heusler Compounds Applied to Magnetocalorics by Monte Carlo Simulations Starting from Ab‐Initio. Issue 14 (12th March 2020)

• Record Type: Journal Article
• Authors: Amorim, Carlos O.; Gonçalves, João N.; Amaral, Vítor S.; Amaral, João S.
• Is Part Of: European journal of inorganic chemistry. Issue 14(2020)
• Page Start: 1271
• Page End: 1277
• Publication Date: 2020-03-12
• Subjects: Monte Carlo simulations -- Magnetocalorics -- Heusler compounds -- Thermodynamics -- Computational chemistry; Chemistry, Inorganic -- Periodicals; Organometallic chemistry -- Periodicals; Bioinorganic chemistry -- Periodicals; Solid state chemistry -- Periodicals; 546
• DOI: 10.1002/ejic.202000046
• Languages: English
• ISSNs: 1434-1948

Abstract

Abstract : Heusler compounds are one of the most versatile families of materials, showing a wide range of technological applications. Here we present a pure in‐silico approach to fully study the physical properties of this promising family of materials as potential competitive magnetocalorics. The temperature and field dependence of the magnetization of three ternary Heusler compounds (Co2 CrGa, Co2 FeGa and Co2 MnGa), as well as of two quaternary Heusler types (Co2 Cr x Y1– x Ga, Y=Fe and Mn) was simulated using DFT ab‐initio and Random Path Sampling Monte Carlo methods. These approaches also allowed the calculation of their corresponding Curie temperatures and their isothermal magnetic entropy change. This manuscript also illustrates a proof of concept of guiding magnetocaloric research using purely computational resources, through the synergy between ab‐initio and the Random Path Sampling, in line with recent high‐throughput computational materials design approaches. Abstract : From ab‐initio calculations as starting point, relevant thermodynamic properties for magnetocaloric research, such as temperature and magnetic field dependence of magnetization, and magnetic entropy change, were predicted for Co2 Cr x Y1– x Ga, (Y = Fe and Mn) Heusler alloys by determining the partition function using the Random Path Sampling method.