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pro vyhledávání: '"Antono, Erin"'
Autor:
Hegde, Vinay I., Borg, Christopher K. H., del Rosario, Zachary, Kim, Yoolhee, Hutchinson, Maxwell, Antono, Erin, Ling, Julia, Saxe, Paul, Saal, James E., Meredig, Bryce
A central challenge in high throughput density functional theory (HT-DFT) calculations is selecting a combination of input parameters and post-processing techniques that can be used across all materials classes, while also managing accuracy-cost trad
Externí odkaz:
http://arxiv.org/abs/2007.01988
Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a giv
Externí odkaz:
http://arxiv.org/abs/1911.11201
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance,
Externí odkaz:
http://arxiv.org/abs/1911.03224
Autor:
Hutchinson, Maxwell L., Antono, Erin, Gibbons, Brenna M., Paradiso, Sean, Ling, Julia, Meredig, Bryce
Despite increasing focus on data publication and discovery in materials science and related fields, the global view of materials data is highly sparse. This sparsity encourages training models on the union of multiple datasets, but simple unions can
Externí odkaz:
http://arxiv.org/abs/1711.05099
Autor:
Ling, Julia, Hutchinson, Maxwell, Antono, Erin, DeCost, Brian, Holm, Elizabeth A., Meredig, Bryce
As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materia
Externí odkaz:
http://arxiv.org/abs/1711.00404
Publikováno v:
Integrating Materials and Manufacturing Innovation, (2017)
The optimization of composition and processing to obtain materials that exhibit desirable characteristics has historically relied on a combination of scientist intuition, trial and error, and luck. We propose a methodology that can accelerate this pr
Externí odkaz:
http://arxiv.org/abs/1704.07423
Autor:
Huang, Ruiyun, Antono, Erin, Meredig, Bryce, Mulholland, Gregory J., Davenport, Timothy C., Haile, Sossina M.
Publikováno v:
In Solid State Ionics 1 October 2021 368
Akademický článek
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Publikováno v:
Journal of Chemical Physics; 7/14/2020, Vol. 153 Issue 2, p1-13, 13p, 1 Chart, 8 Graphs, 2 Maps
Publikováno v:
In Organic Electronics May 2013 14(5):1330-1336