Speaker
Description
Machine learning methods have proven useful for addressing a wide range of tasks in science and technology. Recent applications in materials science include materials discovery, predicting material properties, such as the Curie temperature, constructing phase diagrams, accelerating first-principles calculations, and more. One of the challenges in magnetism theory is accurately determining the magnetic interactions of a system to describe experimental observations and obtain a reliable description of the behavior of magnetic materials. To achieve this, density functional theory (DFT) is typically the method of choice. The resulting magnetic interactions are then used in further simulations employing Monte Carlo, atomistic spin dynamics, or micromagnetic methods. However, the calculated interactions may differ significantly depending on the DFT approach used, making benchmarking against experimental results more difficult.
Recently, researchers have begun extracting exchange interactions directly from experimental data using magnetic images instead of calculating them [1]. Using this approach, the Dzyaloshinskii-Moriya interaction has been extracted with good accuracy and validated through experimental measurements, and several magnetic interactions have been determined simultaneously. Due to the scarcity of experimental data and the difficulty of generating sufficiently large and systematically varied experimental datasets, simulated data have been used to train machine learning models, which are then validated using experimental images.
However, in addition to images, other information is available, including time-series data from so-called pump-probe experiments [2]. In our approach, we use time-series data rather than images to predict the magnetic interactions of various materials and multilayers [3]. Importantly, we address the black-box problem by applying machine-learning explainability methods to assess how the model makes decisions and, consequently, how likely it is to perform well on unseen data. We also examine the information used by the model and evaluate whether it can be understood from a physics perspective.
[1] M. Kawaguchi, et al., Npj Comput. Mater. 7, 20 (2021).
[2] M. Pankratova, et al. Scientific Reports 14, 8138 (2024).
[3] M. Pankratova, et al., Journal of Physics: Condensed Matter. 36 (2024)