Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Michael W, Gaultois"'
Autor:
Andrij Vasylenko, Dmytro Antypov, Vladimir V. Gusev, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-10 (2023)
Abstract The unique nature of constituent chemical elements gives rise to fundamental differences in materials. Assessing materials based on their phase fields, defined as sets of constituent elements, before specific differences emerge due to compos
Externí odkaz:
https://doaj.org/article/6ed7d16fbf1d46259ea4dc5be79bd721
Publikováno v:
Advanced Science, Vol 10, Iss 36, Pp n/a-n/a (2023)
Abstract Glasses frequently reveal structural relaxation that leads to changes in their physical properties including enthalpy, specific volume, and resistivity. Analyzing the short‐range order (SRO) obtained from electron diffraction by transmissi
Externí odkaz:
https://doaj.org/article/ab52ecc0abe94d5abce8e56fee9e8ca4
Autor:
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, Matthew S. Dyer
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-14 (2023)
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 measur
Externí odkaz:
https://doaj.org/article/00a1f5eb159d46b38f5400b22e37fc41
Autor:
Marita O’Sullivan, Jonathan Alaria, Matthew S. Dyer, John B. Claridge, Michael W. Gaultois, Matthew J. Rosseinsky
Publikováno v:
APL Materials, Vol 11, Iss 5, Pp 050702-050702-9 (2023)
Epitaxial heterostructures composed of complex correlated metal oxides, grown along specific crystallographic orientations, offer a route to investigating emergent phenomena such as topological states and spin liquids through geometrical lattice engi
Externí odkaz:
https://doaj.org/article/a85363a0955546738950c79335918623
Autor:
Andrij Vasylenko, Jacinthe Gamon, Benjamin B. Duff, Vladimir V. Gusev, Luke M. Daniels, Marco Zanella, J. Felix Shin, Paul M. Sharp, Alexandra Morscher, Ruiyong Chen, Alex R. Neale, Laurence J. Hardwick, John B. Claridge, Frédéric Blanc, Michael W. Gaultois, Matthew S. Dyer, Matthew J. Rosseinsky
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithiu
Externí odkaz:
https://doaj.org/article/ada6eb69fea0458aa467799a13aadb44
Autor:
Samantha Durdy, Cameron J. Hargreaves, Mark Dennison, Benjamin Wagg, Michael Moran, Jon A. Newnham, Michael W. Gaultois, Matthew J. Rosseinsky, Matthew Dyer
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 compu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34061065690f178be5499eb3f0678d5f
https://doi.org/10.26434/chemrxiv-2023-wgdm0
https://doi.org/10.26434/chemrxiv-2023-wgdm0
Autor:
Bernhard T. Leube, Christopher M. Collins, Luke M. Daniels, Benjamin B. Duff, Yun Dang, Ruiyong Chen, Michael W. Gaultois, Troy D. Manning, Frédéric Blanc, Matthew S. Dyer, John B. Claridge, Matthew J. Rosseinsky
Publikováno v:
Chemistry of Materials. 34:4073-4087
Autor:
Samantha Durdy, Michael W. Gaultois, Vladimir V. Gusev, Danushka Bollegala, Matthew J. Rosseinsky
Publikováno v:
Digital Discovery. 1:763-778
With machine learning being a popular topic in current computational materials science literature, creating representations for compounds has become common place. These representations are rarely compared, as evaluating their performance - and the pe
Autor:
Michael W. Gaultois, T. Wesley Surta
Publikováno v:
Comprehensive Inorganic Chemistry III ISBN: 9780128231531
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4896b74aeb93763e78e1b9235c40498
https://doi.org/10.1016/b978-0-12-823144-9.00074-1
https://doi.org/10.1016/b978-0-12-823144-9.00074-1
Autor:
Clare P. Grey, Matthew J. Cliffe, Francesca C. N. Firth, Joshua M Stratford, Michael W. Gaultois, Dean S. Keeble, Yue Wu
Publikováno v:
Journal of the American Chemical Society. 143:19668-19683
The structures of Zr and Hf metal-organic frameworks (MOFs) are very sensitive to small changes in synthetic conditions. One key difference affecting the structure of UiO MOF phases is the shape and nuclearity of Zr or Hf metal clusters acting as nod