Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB$_2$ ceramic
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910484021641216 |
|---|---|
| author | Lin, Shuyao Casillas-Trujillo, Luis Tasnádi, Ferenc Hultman, Lars Mayrhofer, Paul H. Sangiovanni, Davide G. Koutná, Nikola |
| author_facet | Lin, Shuyao Casillas-Trujillo, Luis Tasnádi, Ferenc Hultman, Lars Mayrhofer, Paul H. Sangiovanni, Davide G. Koutná, Nikola |
| contents | Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects -- governing plasticity and crack nucleation in most materials -- are too large to be included in the training set. Using TiB$_2$ as a model ceramic material, we propose a strategy for fitting MLIPs suitable to simulate mechanical response of monocrystals until fracture. Our MLIP accurately reproduces ab initio stresses and failure mechanisms during room-temperature uniaxial tensile deformation of TiB$_2$ at the atomic scale ($\approx{10}^3$ atoms). More realistic tensile tests (low strain rate, Poisson's contraction) at the nanoscale ($\approx{10}^4$--10$^6$ atoms) require MLIP up-fitting, i.e. learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. To identify useful environments for further up-fitting, i.e., making the MLIP applicable to a wider spectrum of simulations, we asses transferability to other deformation conditions and phases not explicitly trained on. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_00996 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB$_2$ ceramic Lin, Shuyao Casillas-Trujillo, Luis Tasnádi, Ferenc Hultman, Lars Mayrhofer, Paul H. Sangiovanni, Davide G. Koutná, Nikola Materials Science Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of mechanical properties and fracture, however, is far from trivial since extended defects -- governing plasticity and crack nucleation in most materials -- are too large to be included in the training set. Using TiB$_2$ as a model ceramic material, we propose a strategy for fitting MLIPs suitable to simulate mechanical response of monocrystals until fracture. Our MLIP accurately reproduces ab initio stresses and failure mechanisms during room-temperature uniaxial tensile deformation of TiB$_2$ at the atomic scale ($\approx{10}^3$ atoms). More realistic tensile tests (low strain rate, Poisson's contraction) at the nanoscale ($\approx{10}^4$--10$^6$ atoms) require MLIP up-fitting, i.e. learning from additional ab initio configurations. Consequently, we elucidate trends in theoretical strength, toughness, and crack initiation patterns under different loading directions. To identify useful environments for further up-fitting, i.e., making the MLIP applicable to a wider spectrum of simulations, we asses transferability to other deformation conditions and phases not explicitly trained on. |
| title | Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB$_2$ ceramic |
| topic | Materials Science |
| url | https://arxiv.org/abs/2309.00996 |