| Multiscale modeling of the effects of nanoscale load transfer on the effective elastic properties of unfunctionalized carbon nanotube–polyethylene nanocomposites Y Li, GD Seidel Modelling and Simulation in Materials Science and Engineering 22 (2), 025023, 2014 | 77 | 2014 |
| Multiscale modeling of the interface effects in CNT-epoxy nanocomposites Y Li, GD Seidel Computational Materials Science 153, 363-381, 2018 | 62 | 2018 |
| Multiscale modeling of functionalized interface effects on the effective elastic material properties of CNT–polyethylene nanocomposites Y Li, GD Seidel Computational Materials Science 107, 216-234, 2015 | 47 | 2015 |
| Effective design space exploration of gradient nanostructured materials using active learning based surrogate models X Chen, H Zhou, Y Li Materials & Design 183, 108085, 2019 | 33 | 2019 |
| Microscale modeling of creep deformation and rupture in Nickel-based superalloy IN 617 at high temperature VT Phan, X Zhang, Y Li, C Oskay Mechanics of Materials 114, 215-227, 2017 | 31 | 2017 |
| Uncertainty quantification of artificial neural network based machine learning potentials Y Li, W Xiao, P Wang ASME International Mechanical Engineering Congress and Exposition 52170 …, 2018 | 25 | 2018 |
| Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints P Bansal, Z Zheng, C Shao, J Li, M Banu, BE Carlson, Y Li Reliability Engineering & System Safety 227, 108711, 2022 | 19 | 2022 |
| Numerical modeling on the delamination-induced capacity degradation of silicon anode Z Zheng, Z Liu, P Wang, Y Li Journal of Energy Storage 43, 103190, 2021 | 12 | 2021 |
| Reliable machine learning potentials based on artificial neural network for graphene A Singh, Y Li Computational Materials Science 227, 112272, 2023 | 11 | 2023 |
| Atomic Edge-Guided Polyethylene Crystallization on Monolayer Two-Dimensional Materials D Zhou, M Fuentes-Cabrera, A Singh, RR Unocic, JMY Carrillo, K Xiao, ... Macromolecules 55 (2), 559-567, 2022 | 10 | 2022 |
| Numerical study on mechanisms of soy protein as a functional modifier for polymer materials Z Zheng, C Xin, Y Li Modelling and Simulation in Materials Science and Engineering 27 (8), 085010, 2019 | 10 | 2019 |
| Uncertainty quantification of atomistic materials simulation with machine learning potentials Y Li, P Wang, W Xiao 2018 AIAA Non-Deterministic Approaches Conference, 2166, 2018 | 10 | 2018 |
| Analysis of the interface in CNT-polyethylene nanocomposites using a multiscale modeling method Y Li, G Seidel 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials …, 2012 | 10 | 2012 |
| Corrosion of Al-Fe self-pierce riveting joints with multiphysics-based modeling and experiments P Bansal, Z Zheng, B Pan, Y Meng, W Wen, M Banu, J Li, BE Carlson, ... Journal of Manufacturing Processes 95, 434-445, 2023 | 8 | 2023 |
| Machine learning assisted design for active cathode materials S Yong, Z Zheng, P Wang, Y Li ASME International Mechanical Engineering Congress and Exposition 84508 …, 2020 | 8 | 2020 |
| Uncertainty Quantification on Galvanic Corrosion Based on Adaptive Surrogate Modeling P Bansal, Z Zheng, Y Li ASME International Mechanical Engineering Congress and Exposition 86717 …, 2022 | 7 | 2022 |
| Development of artificial neural network potential for graphene A Singh, X Chen, Y Li, S Koric, E Guleryuz AIAA Scitech 2020 Forum, 1861, 2020 | 7 | 2020 |
| Multiphysics Modeling on the Capacity Degradation of Silicon Anode P Bansal, Z Zhang, P Wang, Y Li AIAA SCITECH 2023 Forum, 0772, 2023 | 6 | 2023 |
| Machine Learning Potentials for Graphene A Singh, Y Li ASME International Mechanical Engineering Congress and Exposition 86656 …, 2022 | 6 | 2022 |
| Life cycle assessment of hydrometallurgical recycling for cathode active materials Z Liu, JG Sederholm, KW Lan, EJ Cho, MJ Dipto, Y Gurumukhi, KF Rabbi, ... Journal of Power Sources 580, 233345, 2023 | 5 | 2023 |
Gary D. SeidelVirginia TechBestätigte E-Mail-Adresse bei vt.edu
