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Your search keyword '"Cameron J. Hargreaves"' showing total 4 results
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4 results on '"Cameron J. Hargreaves"'

1. A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Author

Cameron J. Hargreaves, Michael W. Gaultois, Luke M. Daniels, Emma J. Watts, Vitaliy A. Kurlin, Michael Moran, Yun Dang, Rhun Morris, Alexandra Morscher, Kate Thompson, Matthew A. Wright, Beluvalli-Eshwarappa Prasad, Frédéric Blanc, Chris M. Collins, Catriona A. Crawford, Benjamin B. Duff, Jae Evans, Jacinthe Gamon, Guopeng Han, Bernhard T. Leube, Hongjun Niu, Arnaud J. Perez, Aris Robinson, Oliver Rogan, Paul M. Sharp, Elvis Shoko, Manel Sonni, William J. Thomas, Andrij Vasylenko, Lu Wang, Matthew J. Rosseinsky, and Matthew S. Dyer

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
Abstract

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Published
2023
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2. The Liverpool Materials Discovery Server: A suite of computational tools for the collaborative discovery of materials

Author

Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg, Michael Moran, Jon A. Newnham, Michael W. Gaultois, Matthew J. Rosseinsky, and Matthew Dyer

Abstract

The discovery of new materials often requires collaboration between experimental and computational chemists. Web based platforms allow more flexibility in this collaboration by giving access to computational tools without the need for access to computational researchers. We present Liverpool Materials Discovery Server (lmds.liverpool.ac.uk), one such platform which currently hosts six state of the art computational tools in an easy to use format. We describe the development of this platform, highlighting the advantages and disadvantages the methods used. In addition, we provide source code, and setup scripts to enable other research groups to create similar platforms, to promote collaboration both within and between research groups.

Published
2023
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3. A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning.

Author

Hargreaves, Cameron J., Gaultois, Michael W., Daniels, Luke M., Watts, Emma J., Kurlin, Vitaliy A., Moran, Michael, Dang, Yun, Morris, Rhun, Morscher, Alexandra, Thompson, Kate, Wright, Matthew A., Prasad, Beluvalli-Eshwarappa, Blanc, Frédéric, Collins, Chris M., Crawford, Catriona A., Duff, Benjamin B., Evans, Jae, Gamon, Jacinthe, Han, Guopeng, and Leube, Bernhard T.

Subjects
CONDUCTIVITY of electrolytes, SUPERIONIC conductors, MACHINE learning, SOLID electrolytes, POLYMERIC membranes, IONIC conductivity, LITHIUM ions
Abstract

The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors. [ABSTRACT FROM AUTHOR]

Published
2023
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4. Study Results from University of Liverpool Provide New Insights into Machine Learning (A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning)

Subjects
Electrolytes -- Analysis -- Electric properties -- Reports, Machine learning -- Reports -- Electric properties -- Analysis, Computers, University of Liverpool -- Reports
Abstract

2023 FEB 7 (VerticalNews) -- By a News Reporter-Staff News Editor at Information Technology Newsweekly -- New study results on artificial intelligence have been published. According to news reporting from [...]

Published
2023

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