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5 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. 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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3. Lesbian and gay parents and their children : research on the family life cycle

Author

Goldberg, Abbie E. and Goldberg, Abbie E.

Subjects
Lesbian mothers, Gay parents
Abstract

The past several decades have seen increasing controversies over lesbian and gay parenthood. More same-sex couples than ever are becoming parents, building their families while others debate their legal rights pertaining to marriage and parenthood. Against this sociopolitical backdrop, how do same-sex couples transition to parenthood, and what are their experiences as parents? Furthermore, what are the experiences of their children? This book provides a comprehensive overview of the research on same-sex parenthood, exploring ways in which lesbian and gay parents resist; accommodate; and transform fundamental notions of gender, parenting, and family. It takes a family life cycle approach, beginning with research on how same-sex couples meet and build healthy relationships, then describing how and why same-sex couples decide to have children and how they grapple with the changing roles each partner must adopt. Their experiences raising children through young adulthood are explored, including the challenges of interacting with their children's schools and teachers. In the end, Goldberg consider the perspectives of the children themselves---as young adults and adults speak out about their experiences having lesbian or gay parents. Integrating both qualitative and quantitative research, this book incorporates a range of disciplines, including psychology, sociology, sexuality/gender studies, and human development. It also highlights understudied aspects of same-sex parenting, such as termination of couple relationships. With practical recommendations in every chapter, this book is an indispensable resource for those who research lesbian and gay mental health and family issues, as well as those who provide services to lesbian and gay parents and parents-to-be.

Published
2010

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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