| Using deep learning to predict fracture patterns in crystalline solids YC Hsu, CH Yu, MJ Buehler Matter 3 (1), 197-211, 2020 | 121 | 2020 |
| Deep learning model to predict fracture mechanisms of graphene AJ Lew, CH Yu, YC Hsu, MJ Buehler npj 2D Materials and Applications 5 (1), 48, 2021 | 55 | 2021 |
| Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input YC Hsu, Z Yang, MJ Buehler APL Materials 10 (4), 2022 | 41 | 2022 |
| Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model Z Yang, YC Hsu, MJ Buehler Journal of the Mechanics and Physics of Solids 170, 105098, 2023 | 22 | 2023 |
| Tuning mechanical properties in polycrystalline solids using a deep generative framework YC Hsu, CH Yu, MJ Buehler Advanced Engineering Materials 23 (4), 2001339, 2021 | 18 | 2021 |
| DyFraNet: Forecasting and backcasting dynamic fracture mechanics in space and time using a 2D-to-3D deep neural network YC Hsu, MJ Buehler APL Machine Learning 1 (2), 2023 | 7 | 2023 |
| Learning from Nature to Achieve Material Sustainability: Generative AI for Rigorous Bio-inspired Materials Design RK Luu, S Arevalo, W Lu, B Ni, Z Yang, SC Shen, J Berkovich, YC Hsu, ... MIT, 2024 | 5 | 2024 |
| Semi-analytical solution for the generalized absorbing boundary condition in molecular dynamics simulations CS Lee, YY Chen, CH Yu, YC Hsu, CS Chen Computational Mechanics 60, 23-37, 2017 | 5 | 2017 |
Markus J. BuehlerMassachusetts Institute of Technology在 MIT.EDU 的电子邮件经过验证
