Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity
One of the intriguing features exhibited by the layered MAX phase compounds, is the nonlinear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigat...
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IOP Publishing
2025-01-01
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| Series: | JPhys Materials |
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| Online Access: | https://doi.org/10.1088/2515-7639/ada996 |
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| author | Aishwaryo Ghosh Amitava Moitra Tanusri Saha-Dasgupta |
| author_facet | Aishwaryo Ghosh Amitava Moitra Tanusri Saha-Dasgupta |
| author_sort | Aishwaryo Ghosh |
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| description | One of the intriguing features exhibited by the layered MAX phase compounds, is the nonlinear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigation remained a challenge due to the associated length and time scales of the phenomena. In the present work, we adopt a data driven approach to develop a machine learned interatomic potential for the MAX compound Ti _2 AlC following the moment tensor potential protocol. The constructed potential is validated in lattice constant, formation energy, elastic constant, and stacking fault energies. Finally, applying machine learned potential in classical molecular dynamics provides a faithful representation of the experimentally observed nonlinear elasticity for Ti _2 AlC. The generated atomic configurations confirm the proposal of formation of ripplocations which allow atomic layers to glide relative to each other without breaking the in-plane bonds. We find common defects, like Al vacancy, strongly influence the hysteresis properties of the stress–strain curve, paving the route to defect-engineered nonlinear elasticity. |
| format | Article |
| id | doaj-art-aa551617f15a46cbbda0a06dbe8bdba3 |
| institution | Kabale University |
| issn | 2515-7639 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Materials |
| spelling | doaj-art-aa551617f15a46cbbda0a06dbe8bdba32025-02-03T14:43:04ZengIOP PublishingJPhys Materials2515-76392025-01-018202500110.1088/2515-7639/ada996Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticityAishwaryo Ghosh0https://orcid.org/0009-0002-1817-1580Amitava Moitra1Tanusri Saha-Dasgupta2https://orcid.org/0000-0001-6933-3151S.N. Bose National Centre for Basic Sciences , JD Block, Sector III, Salt Lake, Kolkata 700106, IndiaDepartment of Physics, Computational Materials Modelling Center, Raidighi College , Diamond Harbor, West Bengal 743383, IndiaS.N. Bose National Centre for Basic Sciences , JD Block, Sector III, Salt Lake, Kolkata 700106, IndiaOne of the intriguing features exhibited by the layered MAX phase compounds, is the nonlinear elastic behaviour. Since the experimental observation of this curious behaviour, the underlying micro-mechanism has been discussed to interpret experimental observations. However, the theoretical investigation remained a challenge due to the associated length and time scales of the phenomena. In the present work, we adopt a data driven approach to develop a machine learned interatomic potential for the MAX compound Ti _2 AlC following the moment tensor potential protocol. The constructed potential is validated in lattice constant, formation energy, elastic constant, and stacking fault energies. Finally, applying machine learned potential in classical molecular dynamics provides a faithful representation of the experimentally observed nonlinear elasticity for Ti _2 AlC. The generated atomic configurations confirm the proposal of formation of ripplocations which allow atomic layers to glide relative to each other without breaking the in-plane bonds. We find common defects, like Al vacancy, strongly influence the hysteresis properties of the stress–strain curve, paving the route to defect-engineered nonlinear elasticity.https://doi.org/10.1088/2515-7639/ada996machine learningclassical molecular dynamicselasticity |
| spellingShingle | Aishwaryo Ghosh Amitava Moitra Tanusri Saha-Dasgupta Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity JPhys Materials machine learning classical molecular dynamics elasticity |
| title | Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity |
| title_full | Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity |
| title_fullStr | Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity |
| title_full_unstemmed | Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity |
| title_short | Ab-initio trained machine learning potential for MAX compound Ti2AlC: construction, validation, and study of non linear elasticity |
| title_sort | ab initio trained machine learning potential for max compound ti2alc construction validation and study of non linear elasticity |
| topic | machine learning classical molecular dynamics elasticity |
| url | https://doi.org/10.1088/2515-7639/ada996 |
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