Journal
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
Volume 9, Issue 3, Pages 213-220Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s40192-020-00178-0
Keywords
Machine learning; Band gap; Transfer learning; Ensemble learning
Funding
- National Science Foundation under NSF CAREER Award [1651668]
- Division Of Materials Research
- Direct For Mathematical & Physical Scien [1651668] Funding Source: National Science Foundation
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Many of the machine learning-based approaches for materials property predictions use low-cost computational data. The motivation for machine learning models is based on the orders of magnitude speedup compared to DFT calculations or experimental characterization. High-quality experimental materials data would be ideal for training these models; unfortunately, experimental data are typically costly to obtain. As a result, experimental databases are often smaller and less cohesive. Using band gap, we demonstrate how an ensemble learning approach allows us to efficiently model experimental data by combining models trained on otherwise disparate computational and experimental data. This approach demonstrates how disparate data sources can be incorporated into the modeling of sparsely represented experimental data. In the case of band gap prediction, we reduce the root mean squared error by over 9%.
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