PREVENTING MATERIAL’S FAILURE UNDER EXTREME LOADS
PREVENTING MATERIAL’S FAILURE UNDER EXTREME LOADS
Disciplines
Computer Sciences (25%); Materials Engineering (75%)
Keywords
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MAB phases,
Molecular dynamics,
Machine-learning interatomic potentials,
Mechanical properties,
Fracture,
Ceramics
Advancing materials performance and biocompatibility as well as designing new materials with outstanding property combination are fundamental challenges for technologies and infrastructure we rely onand, ultimatelykey prerequisites for sustainability. Modern engineering materials are commonly exposed to extreme environments, including severe mechanical loads, high temperatures, or high-energy radiation. Preventing their catastrophic failure necessitates atomic-level understanding of the defects and processes activated by the material in response to demanding conditions. This project will focus on ceramic-based materials (such as transition metal nitrides, borides, or the so-called MAB phases) applicable in the field of protective and wear-resistant coatings, magnetic cooling, radiation shielding, electrocatalysis or electrochemical sensing, and will develop insights into their mechanical failure under application-relevant conditions. In terms of methods, finite- temperature atomistic simulations in combination with modern machine-learning methods will be employed, in particular, by exploiting the concept of machine-learning interatomic potentials (MLIPs), which have been a vibrant research topic of the last decade. This approach allows predicting behaviour of complex material systems at scales accessible to high-resolution microscopy, with near quantum mechanical accuracy but significantly reduced computational costs. Among the main projects challenges will be efficient training and validation strategies for MLIPs applicable to nansocale simulations and transferable to a wide range of stressemperature conditions including various crystallographic defects and/or nanolayered architectures. Another challenging task will be the development of simulation setups providing reasonably simple modelsof modern micromechanical experiments, e.g., pillar compression or cantilever bending tests. Atomistic simulations will be complemented by targeted experiments carried out by well established collaborators in Austria or abroad, having access to advanced facilities for thin film growth, characterisation, and micromechanical testing. The acquired knowledge will serve to formulate design guidelines for ceramic-based materials with outstanding resistance to mechanical failure. Atomistic simulations will be complemented by targeted experiments carried out by well established collaborators in Austria or abroad, having access to advanced facilities for thin film growth, characterisation, and micromechanical testing. The acquired knowledge will serve to formulate design guidelines for ceramic-based materials with outstanding resistance to mechanical failure.
- Technische Universität Wien - 100%
Research Output
- 1 Citations
- 1 Publications
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2025
Title Machine-learning potentials for structurally and chemically complex MAB phases: Strain hardening and ripplocation-mediated plasticity DOI 10.1016/j.matdes.2025.114307 Type Journal Article Author Koutná N Journal Materials & Design Pages 114307 Link Publication