| 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 | 287 | 2020 |
| 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 |
| Compositionally restricted attention-based network for materials property predictions AYT Wang, SK Kauwe, RJ Murdock, TD Sparks Npj Computational Materials 7 (1), 77, 2021 | 131 | 2021 |
| 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 | 88 | 2018 |
| Can machine learning find extraordinary materials? SK Kauwe, J Graser, R Murdock, TD Sparks Computational Materials Science 174, 109498, 2020 | 70 | 2020 |
| Data-driven studies of li-ion-battery materials SK Kauwe, TD Rhone, TD Sparks Crystals 9 (1), 54, 2019 | 53 | 2019 |
| Is domain knowledge necessary for machine learning materials properties? RJ Murdock, SK Kauwe, AYT Wang, TD Sparks Integrating Materials and Manufacturing Innovation 9, 221-227, 2020 | 50 | 2020 |
| Machine learning for structural materials TD Sparks, SK Kauwe, ME Parry, AM Tehrani, J Brgoch Annual Review of Materials Research 50, 27-48, 2020 | 37 | 2020 |
| Extracting knowledge from DFT: experimental band gap predictions through ensemble learning SK Kauwe, T Welker, TD Sparks Integrating materials and manufacturing innovation 9 (3), 213-220, 2020 | 30 | 2020 |
| Atomic-scale design protocols toward energy, electronic, catalysis, and sensing applications F Belviso, VEP Claerbout, A Comas-Vives, NS Dalal, FR Fan, A Filippetti, ... Inorganic chemistry 58 (22), 14939-14980, 2019 | 25 | 2019 |
| Benchmark AFLOW data sets for machine learning CL Clement, SK Kauwe, TD Sparks Integrating Materials and Manufacturing Innovation 9 (2), 153-156, 2020 | 24 | 2020 |
| Not Just Par for the Course: 73 Quaternary Germanides RE4M2XGe4 (RE = La–Nd, Sm, Gd–Tm, Lu; M = Mn–Ni; X = Ag, Cd) and the Search for … D Zhang, AO Oliynyk, GM Duarte, AK Iyer, L Ghadbeigi, SK Kauwe, ... Inorganic chemistry 57 (22), 14249-14259, 2018 | 15 | 2018 |
| Enhancing terahertz generation from a two-color plasma using optical parametric amplifier waste light SA Sorenson, CD Moss, SK Kauwe, JD Bagley, JA Johnson Applied Physics Letters 114 (1), 2019 | 14 | 2019 |
| Benchmark datasets incorporating diverse tasks, sample sizes, material systems, and data heterogeneity for materials informatics AN Henderson, SK Kauwe, TD Sparks Data in Brief 37, 107262, 2021 | 11 | 2021 |
| Optimizing fractional compositions to achieve extraordinary properties AR Falkowski, SK Kauwe, TD Sparks Integrating Materials and Manufacturing Innovation 10 (4), 689-695, 2021 | 8 | 2021 |
| Materials Abundance, Price, and Availability Data from the Years 1998 to 2015 B Theler, SK Kauwe, TD Sparks Integrating Materials and Manufacturing Innovation 9, 144-150, 2020 | 8 | 2020 |
| Sequential machine learning applications of particle packing with large size variations JR Hall, SK Kauwe, TD Sparks Integrating Materials and Manufacturing Innovation 10, 559-567, 2021 | 5 | 2021 |
| Skin electrical resistance as a diagnostic and therapeutic biomarker of breast cancer measuring lymphatic regions N Andreasen, H Crandall, O Brimhall, B Miller, J Perez-Tamayo, ... IEEE Access 9, 152322-152332, 2021 | 5 | 2021 |
| Extracting knowledge from DFT: experimental band gap predictions through ensemble learning T Sparks, S Kauwe, T Welker | 5 | 2018 |
| Visualization Tool for Atomic Models (VITAL): A Simple Visualization Tool for Materials Predictions S Kauwe, Y Yang, T Sparks | 2 | 2019 |
