Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Jason Hattrick‐Simpers"'
Autor:
Runze Zhang, Debashish Sur, Kangming Li, Julia Witt, Robert Black, Alexander Whittingham, John R. Scully, Jason Hattrick-Simpers
Publikováno v:
npj Materials Degradation, Vol 8, Iss 1, Pp 1-7 (2024)
Abstract Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the
Externí odkaz:
https://doaj.org/article/7664c7498ea949ee99eba800956e87f9
Autor:
Kamal Choudhary, Daniel Wines, Kangming Li, Kevin F. Garrity, Vishu Gupta, Aldo H. Romero, Jaron T. Krogel, Kayahan Saritas, Addis Fuhr, Panchapakesan Ganesh, Paul R. C. Kent, Keqiang Yan, Yuchao Lin, Shuiwang Ji, Ben Blaiszik, Patrick Reiser, Pascal Friederich, Ankit Agrawal, Pratyush Tiwary, Eric Beyerle, Peter Minch, Trevor David Rhone, Ichiro Takeuchi, Robert B. Wexler, Arun Mannodi-Kanakkithodi, Elif Ertekin, Avanish Mishra, Nithin Mathew, Mitchell Wood, Andrew Dale Rohskopf, Jason Hattrick-Simpers, Shih-Han Wang, Luke E. K. Achenie, Hongliang Xin, Maureen Williams, Adam J. Biacchi, Francesca Tavazza
Publikováno v:
npj Computational Materials, Vol 10, Iss 1, Pp 1-17 (2024)
Abstract Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful be
Externí odkaz:
https://doaj.org/article/dd95e89a6ffb48b6b7fe3dd56910340f
Autor:
Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-10 (2023)
Abstract Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by reveali
Externí odkaz:
https://doaj.org/article/5aa92fdd64c04f02bcaafa9fbaf1283d
Publikováno v:
npj Computational Materials, Vol 9, Iss 1, Pp 1-9 (2023)
Abstract Recent advances in machine learning (ML) have led to substantial performance improvement in material database benchmarks, but an excellent benchmark score may not imply good generalization performance. Here we show that ML models trained on
Externí odkaz:
https://doaj.org/article/2d99df21b9c046d796092b0a0cd6bc28
Autor:
Kangming Li, Daniel Persaud, Kamal Choudhary, Brian DeCost, Michael Greenwood, Jason Hattrick-Simpers
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-1 (2024)
Externí odkaz:
https://doaj.org/article/e134cf776d2945d38b7ac9930b19ee2d
Publikováno v:
Communications Materials, Vol 3, Iss 1, Pp 1-9 (2022)
Machine learning is an increasingly important tool for materials science. Here, the authors suggest that its contextual use, including careful assessment of resources and bias, judicious model selection, and an understanding of its limitations, will
Externí odkaz:
https://doaj.org/article/92ea40334d394a9784760a26b7c35f56
Autor:
Yafei Wang, Bonita Goh, Phalgun Nelaturu, Thien Duong, Najlaa Hassan, Raphaelle David, Michael Moorehead, Santanu Chaudhuri, Adam Creuziger, Jason Hattrick‐Simpers, Dan J. Thoma, Kumar Sridharan, Adrien Couet
Publikováno v:
Advanced Science, Vol 9, Iss 20, Pp n/a-n/a (2022)
Abstract Insufficient availability of molten salt corrosion‐resistant alloys severely limits the fruition of a variety of promising molten salt technologies that could otherwise have significant societal impacts. To accelerate alloy development for
Externí odkaz:
https://doaj.org/article/8a5c1809ab8a4d92add5fedec1f0a1f1
Autor:
A. Gilad Kusne, Heshan Yu, Changming Wu, Huairuo Zhang, Jason Hattrick-Simpers, Brian DeCost, Suchismita Sarker, Corey Oses, Cormac Toher, Stefano Curtarolo, Albert V. Davydov, Ritesh Agarwal, Leonid A. Bendersky, Mo Li, Apurva Mehta, Ichiro Takeuchi
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Machine learning driven research holds big promise towards accelerating materials’ discovery. Here the authors demonstrate CAMEO, which integrates active learning Bayesian optimization with practical experiments execution, for the discovery of new
Externí odkaz:
https://doaj.org/article/04164405f2b441b89a1dbae389eb4d1c
Autor:
Benjamin Ruiz-Yi, Travis Williams, Jonathan Kenneth Bunn, Fang Ren, Naila Al Hasan, Ichiro Takeuchi, Jason Hattrick-Simpers, Apurva Mehta
Publikováno v:
Data in Brief, Vol 34, Iss , Pp 106758- (2021)
The data provided in this article is related to the research article entitled “Phase stabilization and oxidation of a continuous composition spread multi-principal element (AlFeNiTiVZr)1-xCrx alloy” [1]. This data article describes the high-throu
Externí odkaz:
https://doaj.org/article/0d69bbbf9d93466c8d01cd71fda8112d
Autor:
Brad Boyce, Remi Dingreville, Saaketh Desai, Elise Walker, Troy Shilt, Kimberly L. Bassett, Ryan R. Wixom, Aaron P. Stebner, Raymundo Arroyave, Jason Hattrick-Simpers, James A. Warren
Publikováno v:
Matter. 6:1320-1323
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8271023e0007e8f179e7149dce5e34d
https://doi.org/10.1016/j.matt.2023.03.028
https://doi.org/10.1016/j.matt.2023.03.028