Physical Sciences and Mathematics Commons^™

Unlocking The Potential Of Machine Learning In The Derivation Of Low-Energy Models For Metallic Magnets, Vikram Sharma

Doctoral Dissertations

Condensed matter physics often grapples with complex many-particle problems lacking definitive closed-form solutions, necessitating approximation strategies to investigate low-energy sectors of the Hilbert space. Perturbation theory, though widely used for this purpose, is limited when expansion terms diverge. This work introduces a machine learning (ML) assisted protocol to extract effective low-energy models for lattice models of fermions interacting with classical fields, specifically focusing on the Kondo Lattice Model (KLM).

Skyrmions, featuring whirling spin texture and topological protection, are promising candidates for future spintronic devices. Materials featuring conduction electrons coupled to localized $f$-electrons' net moment are ideal for realizing skyrmions and …