| 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 | 2192 | 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 | 1674 | 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 | 755 | 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 | 362 | 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 | 337 | 2018 |
| Method for locating low-energy solutions within B Meredig, A Thompson, HA Hansen, C Wolverton, A Van de Walle Physical Review B—Condensed Matter and Materials Physics 82 (19), 195128, 2010 | 294 | 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 | 263 | 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 | 256 | 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 | 245 | 2018 |
| Highthroughput computational screening of new Liion battery anode materials S Kirklin, B Meredig, C Wolverton Advanced Energy Materials 3 (2), 252-262, 2013 | 227 | 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 | 210 | 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 | 193 | 2016 |
| A hybrid computational–experimental approach for automated crystal structure solution B Meredig, C Wolverton Nature materials 12 (2), 123-127, 2013 | 152 | 2013 |
| First-principles thermodynamic framework for the evaluation of thermochemical - or -splitting materials B Meredig, C Wolverton Physical Review B—Condensed Matter and Materials Physics 80 (24), 245119, 2009 | 151 | 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 | 131 | 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 | 121 | 2016 |
| Overcoming data scarcity with transfer learning ML Hutchinson, E Antono, BM Gibbons, S Paradiso, J Ling, B Meredig arXiv preprint arXiv:1711.05099, 2017 | 117 | 2017 |
| 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 | 114 | 2020 |
| Approaching chemical accuracy with density functional calculations: Diatomic energy corrections S Grindy, B Meredig, S Kirklin, JE Saal, C Wolverton Physical Review B—Condensed Matter and Materials Physics 87 (7), 075150, 2013 | 106 | 2013 |
| 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 | 92 | 2017 |
Chris WolvertonProfessor of Materials Science and Eng., Northwestern University在 northwestern.edu 的电子邮件经过验证
