Spin Glasses: Disorder, Frustration, and Nonequilibrium Complexity

TL;DR

This review comprehensively discusses the fundamental physics, experimental characterization, and recent advances in spin glasses, emphasizing their disordered, frustrated nature and complex out-of-equilibrium dynamics across various materials.

Contribution

It synthesizes theoretical, experimental, and computational perspectives on spin glasses, highlighting new developments in room-temperature materials and modern machine learning approaches.

Findings

Spin glasses exhibit slow dynamics, aging, and memory effects.

Experimental signatures include thermodynamic and dynamical probes.

Recent advances include room-temperature spin glasses and machine learning applications.

Abstract

Spin glasses occupy a unique place in condensed matter: they freeze collectively while remaining struc-turally disordered, and they exhibit slow, history-dependent dynamics that reflect an exceptionally rug-ged free-energy landscape. This review provides an integrated account of spin-glass physics, emphasiz-ing how microscopic ingredients (quenched randomness, frustration, competing exchange interactions, and random fields) conspire to produce macroscopic glassiness. We begin with the canonical Edwards-Anderson and Sherrington-Kirkpatrick formulations to introduce the central theoretical ideas that recur across the literature: extensive degeneracy, metastability, and the emergence of long relaxation times that manifest as aging, memory, and rejuvenation under standard experimental protocols. We then summarize the principal routes used to characterize spin-glass freezing, combining thermodynamic signatures with dynamical probes that reveal the separation of timescales and the sensitivity to thermal and magnetic histories. Building on these foundations, we draw connections across experimental material classes (me-tallic alloys, insulating oxides, and geometrically frustrated systems) by emphasizing how intrinsic ver-sus induced disorder and competing interaction networks shape the observed phenomenology. Recent advances in reentrant and room-temperature spin-glass materials are highlighted as a rapidly developing direction that tests the limits of established paradigms and motivates new materials-driven questions. The review concludes by connecting modern computational developments, including machine-learning phase identification and neural-network analogies, to longstanding challenges in classification, univer-sality, and out-of-equilibrium behavior, and by outlining emerging opportunities at the boundary between classical and quantum spin glasses.