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Small Methods|July 29, 2022
High-Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine LearningZhenze Yang, Markus J BuehlerAdvanced Materials (Deerfield Beach, Fla.)|March 19, 2023
Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field InformationZhenze Yang, Markus J BuehlerScience Advances|April 10, 2021
Deep learning model to predict complex stress and strain fields in hierarchical compositesZhenze Yang, Chi-Hua Yu, Markus J BuehlerMaterials Horizons|November 25, 2021
Artificial intelligence and machine learning in design of mechanical materialsKai Guo, Zhenze Yang, Chi-Hua Yu, et al.Science Advances|March 26, 2025
Learning the rules of peptide self-assembly through data mining with large language modelsZhenze Yang, Sarah K Yorke, Tuomas P J Knowles, et al.JACS Au|November 29, 2021
Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified LearningSheng Gong, Shuo Wang, Taishan Zhu, et al.Arxiv|June 4, 2025
High-throughput Screening of the Mechanical Properties of Peptide AssembliesSarah K Yorke, Zhenze Yang, Aviad Levin, et al.Journal of the Mechanical Behavior of Biomedical Materials|July 25, 2009
Nanomechanics of collagen fibrils under varying cross-link densities: atomistic and continuum studiesMarkus J BuehlerPatterns (New York, N.Y.)|March 24, 2023
Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designsMarkus J BuehlerJournal of Applied Mechanics|November 17, 2022
Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion ModelMarkus J BuehlerPageof 32