Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68378271%3A_____%2F25%3A00616765" target="_blank" >RIV/68378271:_____/25:00616765 - isvavai.cz</a>

Výsledek na webu

<a href="https://hdl.handle.net/11104/0368832" target="_blank" >https://hdl.handle.net/11104/0368832</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.commatsci.2024.113629" target="_blank" >10.1016/j.commatsci.2024.113629</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride

Popis výsledku v původním jazyce

Ab initio calculations represent the technique of election to study material system, however, they present severe limitations in terms of the size of the system that can be simulated. Often, the results in the simulation of amorphous materials depend dramatically on the size of the system. Here, we overcome this limitation for the specific case of mechanical properties of amorphous silicon nitride (a-Si3N4) by training a machine learning (ML) interatomic model. Our strategy is based on the generation of targeted training sets, which also include deliberately stressed structures. Using this dataset, we trained a moment tensor potential (MTP) for a-Si3N4. We show that molecular dynamics simulations using the ML model on much larger systems yield elastically isotropic response and can reproduce experimental measurement. To do so, models containing at least ~3500 atoms are necessary. The Young’s modulus calculated from the MTP at room temperature is 220 GPa, which is very well in agreement with the nanoindentation measurement. Our study demonstrates the broader impact of machine learning potentials for predicting structural and mechanical properties, even for complex amorphous structures.

Název v anglickém jazyce

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: A case study of silicon nitride

Popis výsledku anglicky

Ab initio calculations represent the technique of election to study material system, however, they present severe limitations in terms of the size of the system that can be simulated. Often, the results in the simulation of amorphous materials depend dramatically on the size of the system. Here, we overcome this limitation for the specific case of mechanical properties of amorphous silicon nitride (a-Si3N4) by training a machine learning (ML) interatomic model. Our strategy is based on the generation of targeted training sets, which also include deliberately stressed structures. Using this dataset, we trained a moment tensor potential (MTP) for a-Si3N4. We show that molecular dynamics simulations using the ML model on much larger systems yield elastically isotropic response and can reproduce experimental measurement. To do so, models containing at least ~3500 atoms are necessary. The Young’s modulus calculated from the MTP at room temperature is 220 GPa, which is very well in agreement with the nanoindentation measurement. Our study demonstrates the broader impact of machine learning potentials for predicting structural and mechanical properties, even for complex amorphous structures.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GF19-29679L" target="_blank" >GF19-29679L: Řízení difůze snižující tření nanokompozitních materiálů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Computational Materials Science

ISSN

0927-0256

e-ISSN

1879-0801

Svazek periodika

249

Číslo periodika v rámci svazku

Feb

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

113629

Kód UT WoS článku

001407780600001

EID výsledku v databázi Scopus

2-s2.0-85213968349