Journal
CHEMISTRY OF MATERIALS
Volume 29, Issue 21, Pages 9436-9444Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemmater.7b03500
Keywords
-
Funding
- National Science Foundation Award [1534340]
- DMREF
- Office of Naval Research (ONR) [N00014-16-I-2432]
- MIT Energy Initiative
- NSERC
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1534340] Funding Source: National Science Foundation
Ask authors/readers for more resources
In the past several years, Materials Genome Initiative (MGI) efforts have produced myriad examples of computationally designed materials in the fields of energy storage, catalysis, thermoelectrics, and hydrogen storage as well as large data resources that are used to screen for potentially transformative compounds. The bottleneck in high-throughput materials design has thus shifted to materials synthesis, which motivates our development of a methodology to automatically compile materials synthesis parameters across tens of thousands of scholarly publications using natural language processing techniques. To demonstrate our framework's capabilities, we examine the synthesis conditions for various metal oxides across more than 12 thousand manuscripts. We then apply machine learning methods to predict the critical parameters needed to synthesize titania nanotubes via hydrothermal methods and verify this result against known mechanisms. Finally, we demonstrate the capacity for transfer learning by using machine learning models to predict synthesis outcomes on materials systems not included in the training set and thereby outperform heuristic strategies.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available