Marat Latypov

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Scholarly Contributions

Journals/Publications

• Hestroffer, J. M., Charpagne, M., Latypov, M. I., & Beyerlein, I. J. (2023). Graph neural networks for efficient learning of mechanical properties of polycrystals. Computational Materials Science, 217, 111894.
• Hu, G., & Latypov, M. (2022). Learning from 2D: Machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections. Frontiers in Metals and Alloys, 1-9.
• Pfeiffer, O. P., Liu, H., Montanelli, L., Latypov, M. I., Sen, F. G., Hegadekatte, V., Olivetti, E. A., & Homer, E. R. (2022). Aluminum alloy compositions and properties extracted from a corpus of scientific manuscripts and US patents. Scientific Data, 9(1), 128.
• Foley, D. L., Latypov, M. I., Zhao, X., Hestroffer, J., Beyerlein, I. J., Lamberson, L. E., & Taheri, M. L. (2022). Geometrically necessary dislocation density evolution as a function of microstructure and strain rate. Materials Science and Engineering: A, 831, 142224.
• Sahoo, S. K., Toth, L. S., Molinari, A., Latypov, M. I., & Bouaziz, O. (2022). Plastic energy-based analytical approach to predict the mechanical response of two-phase materials with application to dual-phase steels. European Journal of Mechanics-A/Solids, 91, 104414.

Presentations

• Latypov, M. (2022). "Learning from 2D: Data-driven Model Predicting Bulk Properties Based on 2D Microstructure Sections. TMS 2022. Anaheim, CA.
• Latypov, M. (2022). Machine learning mechanical properties in relation to heterogeneous mesoscale microstructure. Los Alamos - Arizona Days 2022. Los Alamos, NM.
• Latypov, M., & Hu, G. (2022). Invited: Learning from 2D—Machine Learning Models for Effective Properties of Heterogeneous Materials Based on 2D Microstructure Sections . MRS Fall Meeting. Boston, MA.