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Bryce Meredig
Researcher at Northwestern University
Publications - 60
Citations - 6404
Bryce Meredig is an academic researcher from Northwestern University. The author has contributed to research in topics: Materials informatics & Density functional theory. The author has an hindex of 25, co-authored 55 publications receiving 4478 citations. Previous affiliations of Bryce Meredig include Lawrence Livermore National Laboratory.
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Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
TL;DR: The Open Quantum Materials Database (OQMD) as mentioned in this paper contains over 200,000 DFT calculated crystal structures and will be freely available for public use at http://oqmd.org.
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Scott Kirklin,James E. Saal,Bryce Meredig,Alexander Thompson,Jeff W. Doak,Muratahan Aykol,Stephan Ruhl,Chris Wolverton +7 more
TL;DR: The Open Quantum Materials Database (OQMD) as discussed by the authors is a high-throughput database consisting of nearly 300,000 density functional theory (DFT) total energy calculations of compounds from the Inorganic Crystal Structure Database (ICSD).
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Combinatorial screening for new materials in unconstrained composition space with machine learning
Bryce Meredig,Amit Agrawal,Scott Kirklin,James E. Saal,Jeff W. Doak,Alan J Thompson,Kunpeng Zhang,Alok Choudhary,Chris Wolverton +8 more
TL;DR: A machine learning model is constructed from a database of thousands of density functional theory calculations that can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT.
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High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds
Anton O. Oliynyk,Erin Antono,Taylor D. Sparks,Leila Ghadbeigi,Michael W. Gaultois,Bryce Meredig,Arthur Mar +6 more
TL;DR: A machine learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials as discussed by the authors.
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The 2019 materials by design roadmap
Kirstin Alberi,Marco Buongiorno Nardelli,Andriy Zakutayev,Lubos Mitas,Stefano Curtarolo,Stefano Curtarolo,Anubhav Jain,Marco Fornari,Nicola Marzari,Ichiro Takeuchi,Martin L. Green,Mercouri G. Kanatzidis,Michael F. Toney,Sergiy Butenko,Bryce Meredig,Stephan Lany,Ursula R. Kattner,Albert V. Davydov,Eric S. Toberer,Vladan Stevanović,Aron Walsh,Aron Walsh,Nam-Gyu Park,Alán Aspuru-Guzik,Daniel P. Tabor,Jenny Nelson,James Edward Murphy,Anant Achyut Setlur,John M. Gregoire,Hong Li,Ruijuan Xiao,Alfred Ludwig,Lane W. Martin,Lane W. Martin,Andrew M. Rappe,Su-Huai Wei,John D. Perkins +36 more
TL;DR: In this paper, the authors present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed.