Symbolic regression in materials science Y Wang, N Wagner, JM Rondinelli
MRS Communications 9 (3), 793-805, 2019
208 2019 Theory-guided machine learning in materials science N Wagner, JM Rondinelli
Frontiers in Materials 3, 28, 2016
162 2016 Electrochemical cycling of sodium-filled silicon clathrate NA Wagner, R Raghavan, R Zhao, Q Wei, X Peng, CK Chan
ChemElectroChem 1 (2), 2014
42 2014 Type I clathrates as novel silicon anodes: An electrochemical and structural investigation Y Li, R Raghavan, NA Wagner, SK Davidowski, L Baggetto, R Zhao, ...
Advanced Science 2 (6), 1500057, 2015
34 2015 Database, features, and machine learning model to identify thermally driven metal–insulator transition compounds AB Georgescu, P Ren, AR Toland, S Zhang, KD Miller, DW Apley, ...
Chemistry of Materials 33 (14), 5591-5605, 2021
31 2021 Discovery of complex oxides via automated experiments and data science L Yang, JA Haber, Z Armstrong, SJ Yang, K Kan, L Zhou, MH Richter, ...
Proceedings of the National Academy of Sciences 118 (37), e2106042118, 2021
29 2021 Learning from correlations based on local structure: Rare-earth nickelates revisited N Wagner, D Puggioni, JM Rondinelli
Journal of Chemical Information and Modeling 58 (12), 2491-2501, 2018
20 2018 Property control from polyhedral connectivity in oxides N Wagner, R Seshadri, JM Rondinelli
Physical Review B 100 (6), 064101, 2019
13 2019 Symbolic regression in materials science. MRS Commun 9 (3): 793–805 Y Wang, N Wagner, JM Rondinelli
7 2019 A Database and Machine Learning Model to Identify Thermally Driven Metal-Insulator Transition Compounds AB Georgescu, P Ren, AR Toland, EA Olivetti, N Wagner, JM Rondinelli
arXiv preprint arxiv:2010.13306, 2020
2 2020 Platform Infrastructure for Agile Software Estimation NA Wagner
Acquisition Research Program, 2022
2022 A Machine Learning Model and Database for The Identification of New Metal-Insulator Transition Compounds AB Georgescu, P Ren, A Toland, N Wagner, E Olivetti, J Rondinelli
APS March Meeting Abstracts 2021, S44. 006, 2021
2021 Erratum to: Symbolic regression in materials science—CORRIGENDUM Y Wang, N Wagner, JM Rondinelli
MRS Communications 9, 1370-1370, 2019
2019 Corrigendum: Symbolic regression in materials science (MRS Communications (2019) 9 (793 Y Wang, N Wagner, JM Rondinelli
MRS Communications 9 (4), 1370, 2019
2019 Data Science for Design of Functional Materials NA Wagner
Northwestern University, 2019
2019 A Classifier for Metal-Insulator Transitions N Wagner, J Rondinelli
APS March Meeting Abstracts 2019, A18. 007, 2019
2019 Exploring the RNiO3 and RVO3 Phase Diagrams with Data Analytics N Wagner, D Puggioni, J Rondinelli
APS March Meeting Abstracts 2018, S12. 007, 2018
2018 Controlling Elastic Properties in Perovskites with Polyhedral Connectivity N Wagner, J Rondinelli
APS March Meeting Abstracts 2017, S32. 005, 2017
2017 Controlling Spin Ordering in Rare-Earth Perovskite Vanadates N Wagner, J Rondinelli
APS March Meeting Abstracts 2016, Y6. 012, 2016
2016 Structural and Electrochemical Performance of Ternary Type-I Clathrate As Anode Materials for Lithium-Ion Batteries Y Li, R Raghavan, N Wagner, C Chan
Electrochemical Society Meeting Abstracts 226, 252-252, 2014
2014