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@article{Mistry:2021:10.1021/acsenergylett.1c00194,
author = {Mistry, A and Franco, AA and Cooper, SJ and Roberts, SA and Viswanathan, V},
doi = {10.1021/acsenergylett.1c00194},
journal = {ACS Energy Letters},
pages = {1422--1431},
title = {How machine learning will revolutionize electrochemical sciences},
url = {http://dx.doi.org/10.1021/acsenergylett.1c00194},
volume = {6},
year = {2021}
}

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TY  - JOUR
AB  - Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.
AU  - Mistry,A
AU  - Franco,AA
AU  - Cooper,SJ
AU  - Roberts,SA
AU  - Viswanathan,V
DO  - 10.1021/acsenergylett.1c00194
EP  - 1431
PY  - 2021///
SN  - 2380-8195
SP  - 1422
TI  - How machine learning will revolutionize electrochemical sciences
T2  - ACS Energy Letters
UR  - http://dx.doi.org/10.1021/acsenergylett.1c00194
UR  - https://pubs.acs.org/doi/10.1021/acsenergylett.1c00194
UR  - http://hdl.handle.net/10044/1/87956
VL  - 6
ER  -

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Dr Samuel J Cooper

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