| Artificial intelligence and machine learning in design of mechanical materials K Guo, Z Yang, CH Yu, MJ Buehler Materials Horizons 8 (4), 1153-1172, 2021 | 463 | 2021 |
| Deep learning model to predict complex stress and strain fields in hierarchical composites Z Yang, CH Yu, MJ Buehler Science Advances 7 (15), eabd7416, 2021 | 269 | 2021 |
| Metallic Bond-Enabled Wetting Behavior at the Liquid Ga/CuGa2 Interfaces Y Cui, F Liang, Z Yang, S Xu, X Zhao, Y Ding, Z Lin, J Liu ACS applied materials & interfaces 10 (11), 9203-9210, 2018 | 133 | 2018 |
| End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures Z Yang, CH Yu, K Guo, MJ Buehler Journal of the Mechanics and Physics of Solids 154, 104506, 2021 | 106 | 2021 |
| Liquid metal corrosion effects on conventional metallic alloys exposed to eutectic gallium–indium alloy under various temperature states Y Cui, Y Ding, S Xu, Z Yang, P Zhang, W Rao, J Liu International Journal of Thermophysics 39, 1-14, 2018 | 52 | 2018 |
| 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 | 49 | 2022 |
| Hierarchical multiresolution design of bioinspired structural composites using progressive reinforcement learning CH Yu, BY Tseng, Z Yang, CC Tung, E Zhao, ZF Ren, SS Yu, PY Chen, ... Advanced Theory and Simulations 5 (11), 2200459, 2022 | 40 | 2022 |
| Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks Z Yang, MJ Buehler Npj Computational Materials 8 (1), 198, 2022 | 34 | 2022 |
| Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks Z Yang, MJ Buehler Frontiers in Materials 8, 740754, 2021 | 29 | 2021 |
| 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 | 24 | 2023 |
| Screening and understanding Li adsorption on two-dimensional metallic materials by learning physics and physics-simplified learning S Gong, S Wang, T Zhu, X Chen, Z Yang, MJ Buehler, Y Shao-Horn, ... JACS Au 1 (11), 1904-1914, 2021 | 23 | 2021 |
| High‐Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning Z Yang, MJ Buehler Small Methods 6 (9), 2200537, 2022 | 17 | 2022 |
| Fill in the blank: transferrable deep learning approaches to recover missing physical field information Z Yang, MJ Buehler Advanced Materials 35 (23), 2301449, 2023 | 16 | 2023 |
| Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks W Lu, Z Yang, MJ Buehler Journal of Applied Physics 132 (7), 2022 | 15 | 2022 |
| Fracture at the two-dimensional limit B Ni, D Steinbach, Z Yang, A Lew, B Zhang, Q Fang, MJ Buehler, J Lou Mrs Bulletin 47 (8), 848-862, 2022 | 12 | 2022 |
| 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, ... An MIT Exploration of Generative AI, 2024 | 7 | 2024 |
| De novo design of polymer electrolytes using GPT-based and diffusion-based generative models Z Yang, W Ye, X Lei, D Schweigert, HK Kwon, A Khajeh npj Computational Materials 10 (1), 296, 2024 | 6* | 2024 |
| A materials discovery framework based on conditional generative models applied to the design of polymer electrolytes A Khajeh, X Lei, W Ye, Z Yang, L Hung, D Schweigert, HK Kwon Digital Discovery 4 (1), 11-20, 2025 | 5* | 2025 |
| Water contact angles on charged surfaces in aerosols YT Shen, T Lin, ZZ Yang, YF Huang, JY Xu, S Meng Chinese Physics B 31 (5), 056801, 2022 | 2 | 2022 |
| End-to-End Deep Learning Approach to Predict Complex Stress and Strain Fields Directly from Microstructural Images MJ Buehler, CH Yu, Z Yang US Patent App. 17/646,505, 2022 | 1 | 2022 |
Markus J. BuehlerMassachusetts Institute of TechnologyPatvirtintas el. paštas MIT.EDU