Cleavage in SiO2: first-principles and ML-potential study

BERECOVÁ, Valentína; Martin FRIÁK a Jana PAVLŮ. Cleavage in SiO2: first-principles and ML-potential study. In Machine Learning for Materials Discovery (ML4MD) workshop. 2025.

Základní údaje

Originální název

Cleavage in SiO2: first-principles and ML-potential study

Autoři

BERECOVÁ, Valentína; Martin FRIÁK a Jana PAVLŮ ORCID

Vydání

Machine Learning for Materials Discovery (ML4MD) workshop, 2025

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Další údaje

Jazyk

angličtina

Typ výsledku

Konferenční abstrakt

Obor

10403 Physical chemistry

Stát vydavatele

Finsko

Utajení

není předmětem státního či obchodního tajemství

Označené pro přenos do RIV

Ne

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

DFT; ab initio calculations; SiO2; Flint cleavage; Machine-learned force fields (MLFF)

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Anotace

V originále

The cleavage processes of flint (SiO2) are fundamental to its mechanical properties, including its exceptional fracture toughness, sharpness retention, and wear resistance. Flint’s microstructure is composed predominantly of tightly packed cryptocrystalline quartz, which contributes to its unique ability to undergo controlled conchoidal fracture. In modern contexts, a detailed understanding of flint’s cleavage processes provides valuable insights for developing advanced materials with similar properties, such as wear-resistant ceramics and cutting tools. By investigating the interatomic interactions that allow cracks to propagate smoothly through the material, producing sharp and durable edges, we can gain valuable insights for both understanding historical artifacts and engineering advancements. In this work, we aim to investigate the fundamental mechanisms of cleavage in flint using density functional theory (DFT) and machine learned force fields (MLFF) as implemented in the Vienna Ab initio Simulation Package (VASP). These trained force fields will then be used in molecular dynamics calculations to explore the cleavage process through multiple SiO2 calculation cells.