Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73631058" target="_blank" >RIV/61989592:15310/25:73631058 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989592:15640/25:73631058 RIV/61989100:27740/25:10257793

Výsledek na webu

<a href="https://www.nature.com/articles/s41524-025-01637-y" target="_blank" >https://www.nature.com/articles/s41524-025-01637-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41524-025-01637-y" target="_blank" >10.1038/s41524-025-01637-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials

Popis výsledku v původním jazyce

The rising demand for digital storage and environmental concerns necessitate ultra-high-density, energy-efficient solutions. Atomic-scale magnets (ASMs) based on transition metal (TM) dimers on defective graphene exhibit promising magnetic anisotropy energy (MAE) values, providing a robust barrier against magnetization reversal. However, identifying optimal TM-substrate configurations is challenging when relying solely on density functional theory (DFT) calculations with spin-orbit coupling. To address this, we developed a machine learning (ML) model trained on scalar-relativistic DFT data using a tree-based gradient boosting approach. Our model implicitly captures key physical interactions from second-order perturbation theory, ensuring reliable MAE predictions for systems beyond the training set. By bridging computational efficiency with interpretability, this work contributes to the development of ASMs for spintronics and quantum materials, offering a pathway to next-generation data storage technologies.

Název v anglickém jazyce

Interpretable machine learning for atomic scale magnetic anisotropy in quantum materials

Popis výsledku anglicky

The rising demand for digital storage and environmental concerns necessitate ultra-high-density, energy-efficient solutions. Atomic-scale magnets (ASMs) based on transition metal (TM) dimers on defective graphene exhibit promising magnetic anisotropy energy (MAE) values, providing a robust barrier against magnetization reversal. However, identifying optimal TM-substrate configurations is challenging when relying solely on density functional theory (DFT) calculations with spin-orbit coupling. To address this, we developed a machine learning (ML) model trained on scalar-relativistic DFT data using a tree-based gradient boosting approach. Our model implicitly captures key physical interactions from second-order perturbation theory, ensuring reliable MAE predictions for systems beyond the training set. By bridging computational efficiency with interpretability, this work contributes to the development of ASMs for spintronics and quantum materials, offering a pathway to next-generation data storage technologies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10301 - Atomic, molecular and chemical physics (physics of atoms and molecules including collision, interaction with radiation, magnetic resonances, Mössbauer effect)

Projekt

<a href="/cs/project/EH22_008%2F0004587" target="_blank" >EH22_008/0004587: Technologie za hranicí nanosvěta</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

npj Computational Materials

ISSN

2057-3960

e-ISSN

—

Svazek periodika

11

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

nestránkováno

Kód UT WoS článku

001489927800001

EID výsledku v databázi Scopus

2-s2.0-105005411644