| Matminer: An open source toolkit for materials data mining L Ward, A Dunn, A Faghaninia, NER Zimmermann, S Bajaj, Q Wang, ... Computational Materials Science 152, 60-69, 2018 | 608 | 2018 |
| Accelerated discovery of metallic glasses through iteration of machine learning and high-throughput experiments F Ren, L Ward, T Williams, KJ Laws, C Wolverton, J Hattrick-Simpers, ... Science advances 4 (4), eaaq1566, 2018 | 452 | 2018 |
| Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science JW Jones, JM Antle, B Basso, KJ Boote, RT Conant, I Foster, HCJ Godfray, ... Agricultural systems 155, 269-288, 2017 | 411 | 2017 |
| Climate impacts on global agriculture emerge earlier in new generation of climate and crop models J Jägermeyr, C Müller, AC Ruane, J Elliott, J Balkovic, O Castillo, B Faye, ... Nature Food 2 (11), 873-885, 2021 | 387 | 2021 |
| ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition D Jha, L Ward, A Paul, W Liao, A Choudhary, C Wolverton, A Agrawal Scientific reports 8 (1), 17593, 2018 | 376 | 2018 |
| Machine learning algorithms for modeling groundwater level changes in agricultural regions of the US S Sahoo, TA Russo, J Elliott, I Foster Water Resources Research 53 (5), 3878-3895, 2017 | 326 | 2017 |
| Reputation systems: A survey and taxonomy F Hendrikx, K Bubendorfer, R Chard Journal of Parallel and Distributed Computing 75, 184-197, 2015 | 288 | 2015 |
| The materials data facility: data services to advance materials science research B Blaiszik, K Chard, J Pruyne, R Ananthakrishnan, S Tuecke, I Foster Jom 68 (8), 2045-2052, 2016 | 284 | 2016 |
| Towards a new generation of agricultural system data, models and knowledge products: Information and communication technology SJC Janssen, CH Porter, AD Moore, IN Athanasiadis, I Foster, JW Jones, ... Agricultural systems 155, 200-212, 2017 | 262 | 2017 |
| Jetstream: a self-provisioned, scalable science and engineering cloud environment CA Stewart, TM Cockerill, I Foster, D Hancock, N Merchant, E Skidmore, ... Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by …, 2015 | 245 | 2015 |
| 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 | 219 | 2018 |
| The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1. 0) J Elliott, C Müller, D Deryng, J Chryssanthacopoulos, KJ Boote, ... Geoscientific Model Development 8 (2), 261-277, 2015 | 212 | 2015 |
| Parsl: Pervasive parallel programming in python Y Babuji, A Woodard, Z Li, DS Katz, B Clifford, R Kumar, L Lacinski, ... Proceedings of the 28th International Symposium on High-Performance Parallel …, 2019 | 207 | 2019 |
| A machine learning approach for engineering bulk metallic glass alloys L Ward, SC O'Keeffe, J Stevick, GR Jelbert, M Aykol, C Wolverton Acta Materialia 159, 102-111, 2018 | 199 | 2018 |
| Autonomous experimentation systems for materials development: A community perspective E Stach, B DeCost, AG Kusne, J Hattrick-Simpers, KA Brown, KG Reyes, ... Matter 4 (9), 2702-2726, 2021 | 190 | 2021 |
| Funcx: A federated function serving fabric for science R Chard, Y Babuji, Z Li, T Skluzacek, A Woodard, B Blaiszik, I Foster, ... Proceedings of the 29th International symposium on high-performance parallel …, 2020 | 153 | 2020 |
| Computing environments for reproducibility: Capturing the “Whole Tale” A Brinckman, K Chard, N Gaffney, M Hategan, MB Jones, K Kowalik, ... Future Generation Computer Systems 94, 854-867, 2019 | 144 | 2019 |
| Predictive big data analytics: a study of Parkinson’s disease using large, complex, heterogeneous, incongruent, multi-source and incomplete observations ID Dinov, B Heavner, M Tang, G Glusman, K Chard, M Darcy, R Madduri, ... PloS one 11 (8), e0157077, 2016 | 139 | 2016 |
| A data ecosystem to support machine learning in materials science B Blaiszik, L Ward, M Schwarting, J Gaff, R Chard, D Pike, K Chard, ... MRS Communications 9 (4), 1125-1133, 2019 | 138 | 2019 |
| Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide G Sivaraman, AN Krishnamoorthy, M Baur, C Holm, M Stan, G Csányi, ... npj Computational Materials 6 (1), 104, 2020 | 125 | 2020 |
Ian T. FosterUniversity of Chicago and Argonne National LaboratoryVerified email at anl.gov
