Machine Learning Interatomic Potentials with Keras API
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911857109893120 |
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| author | Rili, James Paolo |
| author_facet | Rili, James Paolo |
| contents | A neural network is used to train, predict, and evaluate a model to calculate the energies of 3-dimensional systems composed of Ti and O atoms. Python classes are implemented to quantify atomic interactions through symmetry functions and to specify prediction algorithms. The hyperparameters of the model are optimised by minimising validation RMSE, which then produced a model that is accurate to within 100 eV. The model could be improved by proper testing of symmetry function calculations and addressing properties of features and targets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18393 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Interatomic Potentials with Keras API Rili, James Paolo Computational Physics Disordered Systems and Neural Networks A neural network is used to train, predict, and evaluate a model to calculate the energies of 3-dimensional systems composed of Ti and O atoms. Python classes are implemented to quantify atomic interactions through symmetry functions and to specify prediction algorithms. The hyperparameters of the model are optimised by minimising validation RMSE, which then produced a model that is accurate to within 100 eV. The model could be improved by proper testing of symmetry function calculations and addressing properties of features and targets. |
| title | Machine Learning Interatomic Potentials with Keras API |
| topic | Computational Physics Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2404.18393 |