Articoli con mandati relativi all'accesso pubblico - Bryce MeredigUlteriori informazioni
Non disponibile pubblicamente: 1
First-Principles-Assisted Structure Solution: Leveraging Density Functional Theory to Solve Experimentally Observed Crystal Structures
K Michel, B Meredig, L Ward, C Wolverton
Handbook of Materials Modeling: Applications: Current and Emerging Materials …, 2020
Mandati: US Department of Energy
Disponibili pubblicamente: 26
The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
S Kirklin, JE Saal, B Meredig, A Thompson, JW Doak, M Aykol, S Rühl, ...
npj Computational Materials 1 (1), 1-15, 2015
Mandati: US Department of Energy
High-throughput machine-learning-driven synthesis of full-Heusler compounds
AO Oliynyk, E Antono, TD Sparks, L Ghadbeigi, MW Gaultois, B Meredig, ...
Chemistry of Materials 28 (20), 7324-7331, 2016
Mandati: Natural Sciences and Engineering Research Council of Canada, European Commission
The 2019 materials by design roadmap
K Alberi, MB Nardelli, A Zakutayev, L Mitas, S Curtarolo, A Jain, M Fornari, ...
Journal of Physics D: Applied Physics 52 (1), 013001, 2018
Mandati: US National Science Foundation, US Department of Energy, US Department of …
Materials science with large-scale data and informatics: Unlocking new opportunities
J Hill, G Mulholland, K Persson, R Seshadri, C Wolverton, B Meredig
Mrs Bulletin 41 (5), 399-409, 2016
Mandati: US National Science Foundation, US Department of Energy
Understanding thermoelectric properties from high-throughput calculations: trends, insights, and comparisons with experiment
W Chen, JH Pöhls, G Hautier, D Broberg, S Bajaj, U Aydemir, ZM Gibbs, ...
Journal of Materials Chemistry C 4, 4414-4426, 2016
Mandati: US Department of Energy, National Fund for Scientific Research, Belgium …
Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery
B Meredig, E Antono, C Church, M Hutchinson, J Ling, S Paradiso, ...
Molecular Systems Design & Engineering 3 (5), 819-825, 2018
Mandati: US Department of Energy
High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates
J Ling, M Hutchinson, E Antono, S Paradiso, B Meredig
Integrating Materials and Manufacturing Innovation 6, 207-217, 2017
Mandati: US Department of Energy
Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
MW Gaultois, AO Oliynyk, A Mar, TD Sparks, GJ Mulholland, B Meredig
Apl Materials 4 (5), 2016
Mandati: US National Science Foundation, Natural Sciences and Engineering Research …
Data mining our way to the next generation of thermoelectrics
TD Sparks, MW Gaultois, A Oliynyk, J Brgoch, B Meredig
Scripta Materialia 111, 10-15, 2016
Mandati: US National Science Foundation, Natural Sciences and Engineering Research …
Expanded dataset of mechanical properties and observed phases of multi-principal element alloys
CKH Borg, C Frey, J Moh, TM Pollock, S Gorsse, DB Miracle, ON Senkov, ...
Scientific Data 7 (1), 430, 2020
Mandati: US Department of Defense
Robust FCC solute diffusion predictions from ab-initio machine learning methods
H Wu, A Lorenson, B Anderson, L Witteman, H Wu, B Meredig, D Morgan
Computational Materials Science 134, 160-165, 2017
Mandati: US National Science Foundation
Machine learning–based reduce order crystal plasticity modeling for ICME applications
M Yuan, S Paradiso, B Meredig, SR Niezgoda
Integrating Materials and Manufacturing Innovation 7 (4), 214-230, 2018
Mandati: US Department of Energy, US Department of Defense
Strategies for accelerating the adoption of materials informatics
L Ward, M Aykol, B Blaiszik, I Foster, B Meredig, J Saal, S Suram
MRS Bulletin 43 (9), 683-689, 2018
Mandati: US National Science Foundation, US Department of Energy
Revisiting the revised Ag-Pt phase diagram
GLW Hart, LJ Nelson, RR Vanfleet, BJ Campbell, MHF Sluiter, ...
Acta Materialia 124, 325-332, 2017
Mandati: US National Science Foundation, US Department of Energy
Data-driven materials investigations: the next frontier in understanding and predicting fatigue behavior
AD Spear, SR Kalidindi, B Meredig, A Kontsos, JB Le Graverend
Jom 70, 1143-1146, 2018
Mandati: US National Science Foundation, US Department of Defense
Interpretable models for extrapolation in scientific machine learning
ES Muckley, JE Saal, B Meredig, CS Roper, JH Martin
Digital Discovery 2 (5), 1425-1435, 2023
Mandati: US Department of Defense
A formation energy predictor for crystalline materials using ensemble data mining
A Agrawal, B Meredig, C Wolverton, A Choudhary
2016 IEEE 16th international conference on data mining workshops (ICDMW …, 2016
Mandati: US National Science Foundation, US Department of Energy
Quantifying uncertainty in high-throughput density functional theory: A comparison of AFLOW, Materials Project, and OQMD
VI Hegde, CKH Borg, Z del Rosario, Y Kim, M Hutchinson, E Antono, ...
Physical Review Materials 7 (5), 053805, 2023
Mandati: US Department of Energy
Data‐driven glass/ceramic science research: Insights from the glass and ceramic and data science/informatics communities
E De Guire, L Bartolo, R Brindle, R Devanathan, EC Dickey, J Fessler, ...
Journal of the American Ceramic Society 102 (11), 6385-6406, 2019
Mandati: US National Science Foundation, US Department of Energy
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