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.
@article{arxiv.2404.18393,
title = {Machine Learning Interatomic Potentials with Keras API},
author = {James Paolo Rili},
journal= {arXiv preprint arXiv:2404.18393},
year = {2024}
}