| Data-driven review of thermoelectric materials: Performance and resource considerations MW Gaultois, TD Sparks, CKH Borg, R Seshadri, WD Bonificio, DR Clarke Chemistry of Materials 25 (15), 2911-2920, 2013 | 480 | 2013 |
| 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 | 336 | 2016 |
| Machine learning for materials scientists: an introductory guide toward best practices AYT Wang, RJ Murdock, SK Kauwe, AO Oliynyk, A Gurlo, J Brgoch, ... Chemistry of Materials 32 (12), 4954-4965, 2020 | 281 | 2020 |
| A practical field guide to thermoelectrics: Fundamentals, synthesis, and characterization A Zevalkink, DM Smiadak, JL Blackburn, AJ Ferguson, ML Chabinyc, ... Applied Physics Reviews 5 (2), 2018 | 279 | 2018 |
| Machine learning directed search for ultraincompressible, superhard materials A Mansouri Tehrani, AO Oliynyk, M Parry, Z Rizvi, S Couper, F Lin, ... Journal of the American Chemical Society 140 (31), 9844-9853, 2018 | 272 | 2018 |
| 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 | 185 | 2016 |
| Machine learning and energy minimization approaches for crystal structure predictions: a review and new horizons J Graser, SK Kauwe, TD Sparks Chemistry of Materials 30 (11), 3601-3612, 2018 | 164 | 2018 |
| Stable, heat-conducting phosphor composites for high-power laser lighting C Cozzan, G Lheureux, N O’Dea, EE Levin, J Graser, TD Sparks, ... ACS applied materials & interfaces 10 (6), 5673-5681, 2018 | 140 | 2018 |
| 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 | 127 | 2016 |
| Compositionally restricted attention-based network for materials property predictions AYT Wang, SK Kauwe, RJ Murdock, TD Sparks Npj Computational Materials 7 (1), 77, 2021 | 124 | 2021 |
| Thermal conductivity of the gadolinium calcium silicate apatites: Effect of different point defect types Z Qu, TD Sparks, W Pan, DR Clarke Acta Materialia 59 (10), 3841-3850, 2011 | 115 | 2011 |
| Performance and resource considerations of Li-ion battery electrode materials L Ghadbeigi, JK Harada, BR Lettiere, TD Sparks Energy & Environmental Science 8 (6), 1640-1650, 2015 | 110 | 2015 |
| Machine learning prediction of heat capacity for solid inorganics SK Kauwe, J Graser, A Vazquez, TD Sparks Integrating Materials and Manufacturing Innovation 7, 43-51, 2018 | 86 | 2018 |
| Magnetocapacitance as a sensitive probe of magnetostructural changes in NiCr 2 O 4 TD Sparks, MC Kemei, PT Barton, R Seshadri, ED Mun, VS Zapf Physical Review B 89 (2), 024405, 2014 | 77 | 2014 |
| Ceria (Sm3+, Nd3+)/carbonates composite electrolytes with high electrical conductivity at low temperature W Liu, Y Liu, B Li, TD Sparks, X Wei, W Pan Composites Science and Technology 70 (1), 181-185, 2010 | 70 | 2010 |
| Can machine learning find extraordinary materials? SK Kauwe, J Graser, R Murdock, TD Sparks Computational Materials Science 174, 109498, 2020 | 69 | 2020 |
| Cold temperature performance of phase change material based battery thermal management systems L Ghadbeigi, B Day, K Lundgren, TD Sparks Energy Reports 4, 303-307, 2018 | 64 | 2018 |
| Anisotropic Thermal Diffusivity and Conductivity of LaDoped Strontium Niobate Sr2Nb2O7 TD Sparks, PA Fuierer, DR Clarke Journal of the American Ceramic Society 93 (4), 1136-1141, 2010 | 61 | 2010 |
| Data-driven studies of li-ion-battery materials SK Kauwe, TD Rhone, TD Sparks Crystals 9 (1), 54, 2019 | 51 | 2019 |
| Is Domain Knowledge Necessary for Machine Learning Materials Properties? RJ Murdock, AYT Kauwe, Steven K., Wang, TD Sparks Integrating Materials and Manufacturing Innovation, 221-227, 2020 | 49 | 2020 |
Michael W GaultoisLeverhulme Research Centre for Functional Materials Design在 liverpool.ac.uk 的电子邮件经过验证
