Machine Learning Optimization of P-Type Transparent Conducting Films

Lingfei Wei; Xiaojie Xu; Gurudayal; James Bullock; Joel Ager

doi:10.26434/chemrxiv.8145986.v2

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Machine Learning Optimization of P-Type Transparent Conducting Films

Working Paper

Lingfei Wei ,
Xiaojie Xu ,
Gurudayal ,
James Bullock ,
Joel Ager Lawrence Berkeley National Laboratory, University of California Berkeley

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Abstract

Strategic design of experiments combined with machine learning is used to optimize the synthesis of p-type transparent conductors.

Version notes

Revsed version 29 July 2019

Content

Supplementary material

SI TCM ML ChemRxvi 2019-07-29

Version History

Jul 29, 2019 Version 2

May 20, 2019 Version 1

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The content is available under CC BY NC ND 4.0 License CreativeCommons.org

DOI

10.26434/chemrxiv.8145986.v2

Funding

US Department of Energy

Author’s competing interest statement

The authors declare no competing financial interests.

Keywords

transparent conductors

machine learning

support vector regression

design of experiment

Publication

This content is an early or alternative research output and has not been peer-reviewed at the time of posting.

Machine Learning Optimization of P-Type Transparent Conducting Films

Authors

Abstract

Version notes

Content

Supplementary material

Version History

Metrics

License

DOI

Funding

Author’s competing interest statement

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