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

Kabiraj, A and Jain, T and Mahapatra, S (2022) Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature. In: Patterns, 3 (12).

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Official URL: https://doi.org/10.1016/j.patter.2022.100625


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 (10–1,000 K). Global and local analysis of the features confirms the models’ interpretability. We also demonstrate that the TC can be accurately estimated by a purely first-principles-based approach, free from any empirical corrections. © 2022 The Authors

Item Type: Journal Article
Publication: Patterns
Publisher: Cell Press
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Anisotropy; Curie temperature; Deep neural networks; Hamiltonians; Intelligent systems; Ion exchange; Learning systems; Monte Carlo methods, 2d material; Anisotropic Heisenberg models; Automl; Deep learning; Density-functional-theory; Domain problems; DSML 2: proof-of-concept: data science output have been formulated, implemented, and tested for one domain/problem; Machine-learning; Monte Carlo's simulation; Proof of concept; Shapley; Shapley score, Density functional theory
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 02 Feb 2023 10:41
Last Modified: 02 Feb 2023 10:41
URI: https://eprints.iisc.ac.in/id/eprint/79792

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