The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.
Jha, Dipendra, et al. "Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning." Nature Communications, vol. 10, no. 1, Nov. 2019. https://doi.org/10.1038/s41467-019-13297-w
Jha, Dipendra, Choudhary, Kamal, Tavazza, Francesca, et al., "Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning," Nature Communications 10, no. 1 (2019), https://doi.org/10.1038/s41467-019-13297-w
@article{osti_1619594,
author = {Jha, Dipendra and Choudhary, Kamal and Tavazza, Francesca and Liao, Wei-keng and Choudhary, Alok and Campbell, Carelyn and Agrawal, Ankit},
title = {Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning},
annote = {Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself. },
doi = {10.1038/s41467-019-13297-w},
url = {https://www.osti.gov/biblio/1619594},
journal = {Nature Communications},
issn = {ISSN 2041-1723},
number = {1},
volume = {10},
place = {United Kingdom},
publisher = {Nature Publishing Group},
year = {2019},
month = {11}}
KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mininghttps://doi.org/10.1145/3292500.3330703