Designing two-dimensional materials with novel spin degrees of freedom
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Authors
Minch, Peter, Jeffrey
Issue Date
2024-08
Type
Electronic thesis
Thesis
Thesis
Language
en_US
Keywords
Physics
Alternative Title
Abstract
The central aim of this thesis is to explore the interplay between reduced dimensions in two-dimensional (2D) materials and magnetism. How reduced dimensionality of 2D materials affects their spin properties is an active area of research. In this thesis, I explore this topic from the perspective of materials discovery. That is, I investigate which 2D materials exhibit various magnetic states and propose strategies for the accelerated discovery of novel 2D magnets with desirable properties. In the Chapter 3, the magnetic and thermodynamic properties of transition metal dichalcogenides of the form A2X4, based on monolayer Mn2Se4, are investigated using data analytics. In particular, by combining first-principles calculations with machine learning methods the microscopic origins of the magnetocrystalline anisotropy in these materials are elucidated. A large number of candidate transition metal dichalcogenides are explored by varying the chemical compositions of the transition metal (A) sites and the chalcogen (X) sites. The magnetocrystalline anisotropy is investigated by studying the transition between in-plane and out-of-plane magnetization. Using data analytics, we demonstrate that the interplay between the spin-orbit interactions of the chalcogen and transition metal atoms can impact the magnetic behavior. Finally, this investigation resulted in the identification of several novel transition metal dichalcogenides with large anisotropies that are chemically stable. In Chapter 4, magnetic ordering in two-dimensional (2D) materials is investigated using state-of-the-art machine learning models that use a graph-theory framework. A method for predicting the ground state collinear ordering of 2D magnets using machine learning is presented. We find that representing materials as graphs allows us to better learn structure-property relationships by leveraging both the chemical properties of the constituent atoms and the connectivity between those atoms. Graph neural network models are capable of predicting global properties of crystal structure (i.e. graph-wise properties) and local properties of the constituent atoms (i.e. node-wise properties). Physical constraints are embeded into the model by simultaneously making predictions of local and global properties. In particular, the Atomistic Line Graph Neural Network (ALIGNN) architecture is used.
The ALIGNN model is trained on data comprising local and global magnetic moments of 314 2D structures of the form CrAiiBiBiiX6, based on monolayer Cr2Ge2Te6, calculated from first-principles.
By learning the relationships between both local and global magnetic properties, we demonstrate an improvement over models that only consider global magnetic properties. In Chapter 5, noncollinear spin configurations called spin textures are investigated in magnetic materials that break inversion symmetry. More specifically, Janus monolayers are explored for their potential to host skyrmions, a class of topologically protected magnetic textures. High-throughput first-principles calculations are used to screen for Janus monolayers that exhibit large chiral interactions, which can stabilize skyrmions. In addition, the thermodynamic behavior of spin textures in promising candidates are investigated using Monte Carlo (MC) simulations. Further to this, in Chapter 6, a machine learning framework for predicting the Heisenberg model parameters of magnetic materials, that is, the parameters that define the magnetic interactions in the material, is presented. This framework uses equivariant neural networks, a type of symmetry-aware machine learning model. The advantages of using these types of models is discussed, as well as a model architecture that enforces the symmetry rules of magnetic interactions. Preliminary results for a model that predicts the strength of isotropic magnetic interactions trained on the materials investigated in Chapter 5 are presented.
Description
August 2024
School of Science
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY