Taylor D. Sparks
Taylor D. Sparks
Associate Professor of Materials Science and Engineering University of Utah, Salt Lake City, UT
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
Compositionally restricted attention-based network for materials property predictions
AYT Wang, SK Kauwe, RJ Murdock, TD Sparks
Npj Computational Materials 7 (1), 77, 2021
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
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
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
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
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
Can machine learning find extraordinary materials?
SK Kauwe, J Graser, R Murdock, TD Sparks
Computational Materials Science 174, 109498, 2020
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
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
Anisotropic Thermal Diffusivity and Conductivity of La‐Doped Strontium Niobate Sr2Nb2O7
TD Sparks, PA Fuierer, DR Clarke
Journal of the American Ceramic Society 93 (4), 1136-1141, 2010
Data-driven studies of li-ion-battery materials
SK Kauwe, TD Rhone, TD Sparks
Crystals 9 (1), 54, 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
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