| Accelerating the design of lattice structures using machine learning AE Gongora, C Friedman, DK Newton, TD Yee, Z Doorenbos, B Giera, ... Scientific Reports 14 (1), 13703, 2024 | | 2024 |
| Ultralight conductive metallic aerogels F Qian, T Han, M Worsley US Patent 11,938,545, 2024 | | 2024 |
| Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions J Choi, K Bang, S Jang, J Choi, J Ordonez, D Buttler, A Hiszpanski, ... Journal of Materials Chemistry A, 2023 | 4 | 2023 |
| A Machine Learning Framework to Analyze and Optimize the Print Parameters of Direct Ink Writing (DIW) Systems A Gongora, D Newton, T Yee, Z Doorenbos, B Giera, TYJ Han, K Sullivan, ... APS March Meeting Abstracts 2023, M53. 010, 2023 | | 2023 |
| Explainable machine learning in materials science X Zhong, B Gallagher, S Liu, B Kailkhura, A Hiszpanski, TYJ Han npj computational materials 8 (1), 204, 2022 | 90 | 2022 |
| Generative attribute optimization S Liu, T Han, B Kailkhura, D Loveland Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2022 | | 2022 |
| Generative attribute optimization S Liu, T Han, B Kailkhura, D Loveland US Patent 11,436,427, 2022 | | 2022 |
| A strategic approach to machine learning for material science: how to tackle real-world challenges and avoid pitfalls P Karande, B Gallagher, TYJ Han Chemistry of Materials 34 (17), 7650-7665, 2022 | 17 | 2022 |
| Smart nanoscale materials with colloidal core/shell nanoparticles HAN Jinkyu, T Han US Patent App. 17/183,007, 2022 | | 2022 |
| Magnetic nanostructures and composites for millimeter wave absorption HAN Jinkyu, T Han US Patent 11,404,793, 2022 | | 2022 |
| Attribution-driven explanation of the deep neural network model via conditional microstructure image synthesis S Liu, B Kailkhura, J Zhang, AM Hiszpanski, E Robertson, D Loveland, ... ACS omega 7 (3), 2624-2637, 2022 | 2 | 2022 |
| Deep kernels with probabilistic embeddings for small-data learning A Mallick, C Dwivedi, B Kailkhura, G Joshi, TYJ Han Uncertainty in artificial intelligence, 918-928, 2021 | 6 | 2021 |
| A study of real-world micrograph data quality and machine learning model robustness X Zhong, B Gallagher, K Eves, E Robertson, TN Mundhenk, TYJ Han npj Computational Materials 7 (1), 161, 2021 | 8 | 2021 |
| Explaining neural network predictions of material strength TN Mundhenk, IA Palmer, BJ Gallagher, Y Han Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2021 | | 2021 |
| Leveraging uncertainty from deep learning for trustworthy material discovery workflows J Zhang, B Kailkhura, TYJ Han ACS omega 6 (19), 12711-12721, 2021 | 13 | 2021 |
| Predicting energetics materials’ crystalline density from chemical structure by machine learning P Nguyen, D Loveland, JT Kim, P Karande, AM Hiszpanski, TYJ Han Journal of Chemical Information and Modeling 61 (5), 2147-2158, 2021 | 36 | 2021 |
| Crystal structure prediction of energetic materials and a twisted arene with Genarris and GAtor I Bier, D O'Connor, YT Hsieh, W Wen, AM Hiszpanski, TYJ Han, N Marom CrystEngComm 23 (35), 6023-6038, 2021 | 17 | 2021 |
| Mr-gan: Manifold regularized generative adversarial networks for scientific data Q Li, B Kailkhura, R Anirudh, J Zhang, Y Zhou, Y Liang, TYJ Han, ... SIAM Journal on Mathematics of Data Science 3 (4), 1197-1222, 2021 | 3 | 2021 |
| Data-driven materials research enabled by natural language processing and information extraction EA Olivetti, JM Cole, E Kim, O Kononova, G Ceder, TYJ Han, ... Applied Physics Reviews 7 (4), 2020 | 210 | 2020 |
| Automated identification of molecular crystals’ packing motifs D Loveland, B Kailkhura, P Karande, AM Hiszpanski, TYJ Han Journal of Chemical Information and Modeling 60 (12), 6147-6154, 2020 | 5 | 2020 |
Marcus WorsleyLawrence Livermore National Laboratory在 llnl.gov 的电子邮件经过验证
