Unknown

Dataset Information

0

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


ABSTRACT: Monte Carlo (MC) simulation of the classical Heisenberg model has become the de facto tool to estimate the Curie temperature (T C) 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 R 2 scores of nearly 0.99. Since 2D magnetism has a natural tendency toward low T C, learning-from-data is combined with data-from-learning to ensure a nearly uniform final data distribution over a wide range of T C (10-1,000 K). Global and local analysis of the features confirms the models' interpretability. We also demonstrate that the T C can be accurately estimated by a purely first-principles-based approach, free from any empirical corrections.

SUBMITTER: Kabiraj A 

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

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Kabiraj Arnab A   Jain Tripti T   Mahapatra Santanu S  

Patterns (New York, N.Y.) 20221114 12


Monte Carlo (MC) simulation of the classical Heisenberg model has become the de facto tool to estimate the Curie temperature (<i>T</i> <sub>C</sub>) 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 simula  ...[more]

Similar Datasets

| S-EPMC10353725 | biostudies-literature
| S-EPMC2999362 | biostudies-literature
| S-EPMC6465329 | biostudies-literature
| S-EPMC8381692 | biostudies-literature
| S-EPMC10446805 | biostudies-literature
| S-EPMC10257190 | biostudies-literature
| S-EPMC9170136 | biostudies-literature
| S-EPMC8752096 | biostudies-literature
| S-EPMC2998598 | biostudies-literature
| S-EPMC6432162 | biostudies-literature