Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization-Reference-Cited by-同舟云学术

Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization

Published:2018-12-07 Issue:1 Volume:4 Page:187-191
ISSN:2380-8195
Container-title:ACS Energy Letters
language:en
Short-container-title:ACS Energy Lett.

Author:

Krishnamurthy Dilip¹²^ORCID,Weiland Hasso³,Barati Farimani Amir¹,Antono Erin⁴,Green Josh⁵,Viswanathan Venkatasubramanian¹²^ORCID

Affiliation:

1. Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

2. Wilton E. Scott Institute for Energy Innovation, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States

3. Scandium International Mining Corporation, Sparks, Nevada 89431, United States

4. Citrine Informatics, Redwood City, California 94063, United States

5. Citrine Informatics, Pittsburgh, Pennsylvania 15206, United States

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Chemistry (miscellaneous)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsenergylett.8b02278

Reference56 articles.

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2. Powering the planet: Chemical challenges in solar energy utilization

3. Research Update: The materials genome initiative: Data sharing and the impact of collaborative ab initio databases

4. Hutchinson, M. L.; Antono, E.; Gibbons, B. M.; Paradiso, S.; Ling, J.; Meredig, B. Overcoming Data Scarcity with Transfer Learning. arXiv:1711.05099 [cs.LG], 2017.

5. Lipton, Z. C. The Mythos of Model Interpretability. arXiv:1606.03490, 2016.

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