| A general-purpose machine learning framework for predicting properties of inorganic materials L Ward, A Agrawal, A Choudhary, C Wolverton npj Computational Materials 2, 16028, 2016 | 1200 | 2016 |
| 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 | 671 | 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 | 474 | 2018 |
| 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 | 405 | 2018 |
| Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations L Ward, R Liu, A Krishna, VI Hegde, A Agrawal, A Choudhary, ... Physical Review B 96 (2), 024104, 2017 | 362 | 2017 |
| 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 | 237 | 2018 |
| Atomistic calculations and materials informatics: A review L Ward, C Wolverton Current Opinion in Solid State and Materials Science 21 (3), 167-176, 2017 | 233 | 2017 |
| 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 | 214 | 2018 |
| 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 | 147 | 2019 |
| Machine-learning-accelerated high-throughput materials screening: Discovery of novel quaternary Heusler compounds K Kim, L Ward, J He, A Krishna, A Agrawal, C Wolverton Physical Review Materials 2 (12), 123801, 2018 | 105 | 2018 |
| DLHub: Model and data serving for science R Chard, Z Li, K Chard, L Ward, Y Babuji, A Woodard, S Tuecke, ... 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2019 | 99 | 2019 |
| Structural evolution and kinetics in Cu-Zr metallic liquids from molecular dynamics simulations L Ward, D Miracle, W Windl, ON Senkov, K Flores Physical Review B—Condensed Matter and Materials Physics 88 (13), 134205, 2013 | 96 | 2013 |
| The MolSSI QCArchive project: An open‐source platform to compute, organize, and share quantum chemistry data DGA Smith, D Altarawy, LA Burns, M Welborn, LN Naden, L Ward, S Ellis, ... Wiley Interdisciplinary Reviews: Computational Molecular Science 11 (2), e1491, 2021 | 82 | 2021 |
| Feature engineering for machine learning enabled early prediction of battery lifetime NH Paulson, J Kubal, L Ward, S Saxena, W Lu, SJ Babinec Journal of Power Sources 527, 231127, 2022 | 74 | 2022 |
| 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon KM Jablonka, Q Ai, A Al-Feghali, S Badhwar, JD Bocarsly, AM Bran, ... Digital Discovery 2 (5), 1233-1250, 2023 | 62 | 2023 |
| IRNet: A general purpose deep residual regression framework for materials discovery D Jha, L Ward, Z Yang, C Wolverton, I Foster, W Liao, A Choudhary, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 56 | 2019 |
| Enabling deeper learning on big data for materials informatics applications D Jha, V Gupta, L Ward, Z Yang, C Wolverton, I Foster, W Liao, ... Scientific reports 11 (1), 4244, 2021 | 52 | 2021 |
| Principles of the battery data genome L Ward, S Babinec, EJ Dufek, DA Howey, V Viswanathan, M Aykol, ... Joule 6 (10), 2253-2271, 2022 | 51 | 2022 |
| GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics M Zvyagin, A Brace, K Hippe, Y Deng, B Zhang, CO Bohorquez, A Clyde, ... The International Journal of High Performance Computing Applications 37 (6 …, 2023 | 50 | 2023 |
| Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations L Ward, B Blaiszik, I Foster, RS Assary, B Narayanan, L Curtiss MRS Communications 9 (3), 891-899, 2019 | 50 | 2019 |
