Properties in material composition and crystal structures have been explored by density functional theory (DFT) calculations, using databases such as the Open Quantum Materials Database (OQMD). Databases like these have been used currently for the training of advanced machine learning and deep neural network models, the latter providing higher performance when predicting properties of materials. However, current alternatives have shown a deterioration in accuracy when increasing the number of layers in their architecture (over-fitting problem). As an alternative method to address this problem, we have implemented residual neural network architectures based on Merge and Run Networks, IRNet and UNet to improve performance while relaxing the observed network depth limitation. The evaluation of the proposed architectures include a 9:1 ratio to train and test as well as 10 fold cross validation. In the experiments we found that our proposed architectures based on IRNet and UNet are able to obtain a lower Mean Absolute Error (MAE) than current strategies. The full implementation (Python, Tensorflow and Keras) and the trained networks will be available online for community validation and advancing the state of the art from our findings.
Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks
In the present paper, we introduce a new neural network-based tool for the prediction of formation energies of atomic structures based on elemental and structural features of Voronoi-tessellated materials. We provide a concise overview of the connection between the machine learning and the true material–property relationship, how to improve the generalization accuracy by reducing overfitting, how new data can be incorporated into the model to tune it to a specific material system, and preliminary results on using models to preform local structure relaxations.
The present work resulted in three final models optimized for (1) highest test accuracy on the Open Quantum Materials Database (OQMD), (2) performance in the discovery of new materials, and (3) performance at a low computational cost. On a test set of 21,800 compounds randomly selected from OQMD, they achieve a mean absolute error (MAE) of 28, 40, and 42 meV/atom, respectively. The second model provides better predictions in a test case of interest not present in the OQMD, while the third reduces the computational cost by a factor of 8.
We collect our results in a new open-source tool called SIPFENN (Structure-Informed Prediction of Formation Energy using Neural Networks). SIPFENN not only improves the accuracy beyond existing models more »
- Award ID(s):
- 2050069
- Publication Date:
- NSF-PAR ID:
- 10318666
- Journal Name:
- Computational materials science
- ISSN:
- 1879-0801
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1016/j.commatsci.2022.111254
