| Rapid identification of X-ray diffraction patterns based on very limited data by interpretable convolutional neural networks H Wang, Y Xie, D Li, H Deng, Y Zhao, M Xin, J Lin Journal of chemical information and modeling 60 (4), 2004-2011, 2020 | 110 | 2020 |
| Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN Y Dong, D Li, C Zhang, C Wu, H Wang, M Xin, J Cheng, J Lin Carbon 169, 9-16, 2020 | 53 | 2020 |
| A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures JW Su, D Li, Y Xie, T Zhou, W Gao, H Deng, M Xin, J Lin Smart Materials and Structures 30 (1), 015028, 2020 | 33 | 2020 |
| Machine learning assisted rediscovery of methane storage and separation in porous carbon from material literature C Zhang, D Li, Y Xie, D Stalla, P Hua, DT Nguyen, M Xin, J Lin Fuel 290, 120080, 2021 | 15 | 2021 |
| De novo molecule design towards biased properties via a deep generative framework and iterative transfer learning K Sattari, D Li, B Kalita, Y Xie, FB Lighvan, O Isayev, J Lin Digital Discovery 3 (2), 410-421, 2024 | 1 | 2024 |
| Robust Nonlinear Filter Using Adaptive Edgeworth Expansion D Li, M Xin, B Jia 2018 Annual American Control Conference (ACC), 1927-1932, 2018 | 1 | 2018 |
| De Novo Design of Molecules Towards Biased Properties via a Deep Generative Framework and Iterative Transfer Learning K Sattari, D Li, Y Xie, O Isayev, J Lin | | 2023 |
| Robust Nonlinear Distributed Estimation Using Maximum Correntropy D Li, M Xin 2019 American Control Conference (ACC), 3823-3828, 2019 | | 2019 |
Ming XinProfessor, Dept. of Mechanical & Aerospace Engineering, University of Missouri在 missouri.edu 的电子邮件经过验证
