| An open experimental database for exploring inorganic materials A Zakutayev, N Wunder, M Schwarting, JD Perkins, R White, K Munch, ... Scientific data 5 (1), 1-12, 2018 | 158 | 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 | 138 | 2019 |
| Synergistic plasma-assisted electrochemical reduction of nitrogen to ammonia S Kumari, S Pishgar, ME Schwarting, WF Paxton, JM Spurgeon Chemical communications 54 (95), 13347-13350, 2018 | 45 | 2018 |
| 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 | 36 | 2023 |
| Targeting sars-cov-2 with ai-and hpc-enabled lead generation: A first data release Y Babuji, B Blaiszik, T Brettin, K Chard, R Chard, A Clyde, I Foster, Z Hong, ... arXiv preprint arXiv:2006.02431, 2020 | 25 | 2020 |
| Research data infrastructure for high-throughput experimental materials science KR Talley, R White, N Wunder, M Eash, M Schwarting, D Evenson, ... Patterns 2 (12), 2021 | 24 | 2021 |
| Automated algorithms for band gap analysis from optical absorption spectra M Schwarting, S Siol, K Talley, A Zakutayev, C Phillips Materials Discovery 10, 43-52, 2017 | 19 | 2017 |
| Graph network based deep learning of bandgaps XG Li, B Blaiszik, ME Schwarting, R Jacobs, A Scourtas, KJ Schmidt, ... The Journal of Chemical Physics 155 (15), 2021 | 12 | 2021 |
| High throughput experimental materials database A Zakutayev, J Perkins, M Schwarting, R White, K Munch, W Tumas, ... National Renewable Energy Laboratory-Data (NREL-DATA), Golden, CO (United …, 2017 | 9 | 2017 |
| In silico active learning for small molecule properties L Schneider, M Schwarting, J Mysona, H Liang, M Han, PM Rauscher, ... Molecular Systems Design & Engineering 7 (12), 1611-1621, 2022 | 7 | 2022 |
| On the obfuscation of image sensor fingerprints M Schwarting, T Burton, R Yampolskiy 2015 Annual Global Online Conference on Information and Computer Technology …, 2015 | 7 | 2015 |
| Segmentation of tomography datasets using 3D convolutional neural networks J James, N Pruyne, T Stan, M Schwarting, J Yeom, S Hong, P Voorhees, ... Computational Materials Science 216, 111847, 2023 | 4 | 2023 |
| Truth and regret: Large language models, the quran, and misinformation AR Bhojani, M Schwarting Theology and Science 21 (4), 557-563, 2023 | 2 | 2023 |
| HydroNet: Benchmark tasks for preserving intermolecular interactions and structural motifs in predictive and generative models for molecular data S Choudhury, JA Bilbrey, L Ward, SS Xantheas, I Foster, JP Heindel, ... arXiv preprint arXiv:2012.00131, 2020 | 2 | 2020 |
| Towards online steering of flame spray pyrolysis nanoparticle synthesis M Levental, R Chard, JA Libera, K Chard, A Koripelly, JR Elias, ... 2020 IEEE/ACM 2nd Annual Workshop on Extreme-scale Experiment-in-the-Loop …, 2020 | 2 | 2020 |
| Twins in rotational spectroscopy: Does a rotational spectrum uniquely identify a molecule? M Schwarting, NA Seifert, MJ Davis, B Blaiszik, I Foster, K Prozument arXiv preprint arXiv:2404.04225, 2024 | | 2024 |
| Foundry-ML-Software and Services to Simplify Access to Machine Learning Datasets in Materials Science K Schmidt, A Scourtas, L Ward, S Wangen, M Schwarting, I Darling, ... Journal of Open Source Software 9 (93), 5467, 2024 | | 2024 |
| 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon A Imran, M Schwarting, KM Jablonka, Q Ai, A Al-Feghali, S Badhwar, ... RSC, 2023 | | 2023 |
| MatPropXtractor: Generate to Extract A Ajith, M Schwarting, Z Hong, K Chard, I Foster | | 2023 |
| 3D Convolutional Neural Networks for Dendrite Segmentation Using Fine-Tuning and Hyperparameter Optimization J James, N Pruyne, T Stan, M Schwarting, J Yeom, S Hong, P Voorhees, ... arXiv preprint arXiv:2205.01167, 2022 | | 2022 |
