Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB ₂

Chunhui Du; Shuyao Lin; Nikola Koutná; P.H. Mayrhofer

doi:10.1039/d6nr00466k

January 1, 2026NanoscaleOpen Access

Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB ₂

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Key Points

This study aims to simulate the effects of realistic defects on the mechanical properties of TiB2 using machine-learning methods.
Used machine-learning interatomic potentials (MLIP) for simulations.
Performed molecular dynamics simulations to analyze nanoindentation.
Focused on the impact of Ti-rich planar defects and boron vacancies.
Ti-rich planar defects help maintain the hardness of TiB2.
Boron vacancies negatively affect the hardness compared to Ti-rich defects.

Abstract

This study uses machine-learning interatomic potentials (MLIP) and molecular dynamics to simulate nanoindentation on defective TiB 2 . Results reveal that Ti-rich planar defects may preserve hardness of TiB 2 , unlike boron vacancies.

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Cite This Study

Du et al. (2026) studied this question.

synapsesocial.com/papers/6a2ba3fa8101cf8926f02815 https://doi.org/10.1039/d6nr00466k

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Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB ₂

Key Points

Abstract

Discussion

Cite This Study

Towards nano-mechanical simulations of ceramics containing realistic defects via machine-learning potentials: the example of TiB ₂

Key Points

Abstract

Discussion

Cite This Study

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