Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2302.13537 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866917939694796800 |
|---|---|
| author | Cheng, Guanjian Gong, Xin-Gao Yin, Wan-Jian |
| author_facet | Cheng, Guanjian Gong, Xin-Gao Yin, Wan-Jian |
| contents | We introduce a computational method to optimize target physical properties in the full configuration space regarding atomic composition, chemical stoichiometry, and crystal structure. The approach combines the universal potential of the crystal graph neural network and Bayesian optimization. The proposed approach effectively obtains the crystal structure with the strongest atomic cohesion from all possible crystals. Several new crystals with high atomic cohesion are identified and confirmed by density functional theory for thermodynamic and dynamic stability. Our method introduces a novel approach to inverse materials design with additional functional properties for practical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_13537 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion Cheng, Guanjian Gong, Xin-Gao Yin, Wan-Jian Materials Science Machine Learning We introduce a computational method to optimize target physical properties in the full configuration space regarding atomic composition, chemical stoichiometry, and crystal structure. The approach combines the universal potential of the crystal graph neural network and Bayesian optimization. The proposed approach effectively obtains the crystal structure with the strongest atomic cohesion from all possible crystals. Several new crystals with high atomic cohesion are identified and confirmed by density functional theory for thermodynamic and dynamic stability. Our method introduces a novel approach to inverse materials design with additional functional properties for practical applications. |
| title | Global optimization in the discrete and variable-dimension conformational space: The case of crystal with the strongest atomic cohesion |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2302.13537 |