Generative Discovery of Magnetic Insulators under Competing Physical Constraints

TL;DR

This paper presents MagMatLLM, a generative framework combining language models, evolutionary algorithms, and first-principles validation to discover magnetic insulators satisfying multiple physical constraints.

Contribution

It introduces a constraint-guided discovery approach that enforces functional constraints during generation, enabling the identification of novel magnetic insulators in sparse chemical spaces.

Findings

Identified 12 new candidate magnetic insulators.

10 candidates are dynamically stable with finite band gaps and magnetic moments.

Established a general paradigm for multi-objective materials discovery under constraints.

Abstract

Discovering materials that must simultaneously satisfy multiple competing constraints remains a central challenge in computational materials design, particularly in data-scarce regimes where conventional data-driven approaches are least effective. Magnetic insulators represent a stringent example: the electronic conditions that favor magnetic order often also promote metallicity, while insulating behavior suppresses the interactions that stabilize magnetism. As a result, experimentally viable magnetic insulators are rare and difficult to identify through conventional screening. Here, we introduce MagMatLLM, a constraint-guided generative discovery framework that integrates language-model-based crystal generation with evolutionary selection, surrogate screening, and first-principles validation to target simultaneous stability, magnetism, and insulating behavior. Unlike stability-first approaches, the framework enforces functional constraints during generation and selection, steering the search toward sparsely populated regions of materials space defined by competing physical requirements. Using this workflow, we identify twelve previously unreported candidate magnetic insulators, including Tm$_4$Co$_2$Cr$_2$O$_{12}$ and Cr$_4$Nb$_2$O$_{12}$. Of these, ten are dynamically stable by phonon analysis and exhibit finite band gaps and nonzero magnetic moments in spin-polarized density functional theory calculations. Beyond the specific compounds identified here, this work establishes a general constraint-guided paradigm for multi-objective materials discovery in sparse chemical spaces and provides a transferable strategy for the design of quantum materials under competing physical constraints.