Bryce Meredig
Cited by
Cited by
Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD)
JE Saal, S Kirklin, M Aykol, B Meredig, C Wolverton
Jom 65, 1501-1509, 2013
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
Combinatorial screening for new materials in unconstrained composition space with machine learning
B Meredig, A Agrawal, S Kirklin, JE Saal, JW Doak, A Thompson, K Zhang, ...
Physical Review B 89 (9), 094104, 2014
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
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
Method for locating low-energy solutions within DFT+ U
B Meredig, A Thompson, HA Hansen, C Wolverton, A Van de Walle
Physical Review B 82 (19), 195128, 2010
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
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
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
High‐throughput computational screening of new Li‐ion battery anode materials
S Kirklin, B Meredig, C Wolverton
Advanced Energy Materials 3 (2), 252-262, 2013
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
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
A hybrid computational–experimental approach for automated crystal structure solution
B Meredig, C Wolverton
Nature materials 12 (2), 123-127, 2013
First-principles thermodynamic framework for the evaluation of thermochemical H 2 O-or CO 2-splitting materials
B Meredig, C Wolverton
Physical Review B 80 (24), 245119, 2009
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
Materials data infrastructure: a case study of the citrination platform to examine data import, storage, and access
J O’Mara, B Meredig, K Michel
Jom 68 (8), 2031-2034, 2016
Machine learning in materials discovery: confirmed predictions and their underlying approaches
JE Saal, AO Oliynyk, B Meredig
Annual Review of Materials Research 50, 49-69, 2020
Approaching chemical accuracy with density functional calculations: Diatomic energy corrections
S Grindy, B Meredig, S Kirklin, JE Saal, C Wolverton
Physical Review B 87 (7), 075150, 2013
Overcoming data scarcity with transfer learning
ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig
arXiv preprint arXiv:1711.05099, 2017
Building data-driven models with microstructural images: Generalization and interpretability
J Ling, M Hutchinson, E Antono, B DeCost, EA Holm, B Meredig
Materials Discovery 10, 19-28, 2017
The system can't perform the operation now. Try again later.
Articles 1–20