The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
Open accessAbstract
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$$1,643$$1,643observations, the proposed approach yields a mean absolute error (MAE) of$$0.07$$0.07eV/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.
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Recent advances and applications of deep learning methods in materials science
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“They have also been used for transfer learning across datasets for enhanced predictive accuracy on small data 34 , even for different source and target properties 157 , which is especially useful to build predictive models for target properties for which big source datasets may not be readily…”
Self-citationA critical examination of compound stability predictions from machine-learned formation energies
Bartel, C. J., Trewartha, A., Wang, Q., Dunn, A., Jain, A., & Ceder, G. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00362-y · Cited by 277
Innovative Materials Science via Machine Learning
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A review on machine learning-guided design of energy materials
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Metadata
- Authors
- Jha, D., Choudhary, K., Tavazza, F., Liao, W.-K., Choudhary, A., Campbell, C., & Agrawal, A.
- Venue
- Nature Communications
- Published
- 2019-11-22
- Volume
- 10
- Issue
- 1
- Pages
- 5316-5316
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2990015413
- Cited by
- 339
- References
- 59
- FWCI
- 11.55
- Type
- journal article
- Language
- en
- Open access
- gold
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- Jha, D., Choudhary, K., Tavazza, F., Liao, W.-K., Choudhary, A., Campbell, C., & Agrawal, A. (2019). Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning. Nature Communications. https://doi.org/10.1038/s41467-019-13297-w
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