Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature

ABSTRACT: Summary Monte Carlo (MC) simulation of the classical Heisenberg model has become the de facto tool to estimate the Curie temperature (TC) of two-dimensional (2D) magnets. As an alternative, here we develop data-driven models for the five most common crystal types, considering the isotropic and anisotropic exchange of up to four nearest neighbors and the single-ion anisotropy. We sample the 20-dimensional Heisenberg spin Hamiltonian and conceive a bisection-based MC technique to simulate a quarter of a million materials for training deep neural networks, which yield testing R2 scores of nearly 0.99. Since 2D magnetism has a natural tendency toward low TC, learning-from-data is combined with data-from-learning to ensure a nearly uniform final data distribution over a wide range of TC (10–1,0

SUBMITTER: Kabiraj A 

PROVIDER: S-EPMC9782261 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature