Marat Latypov
- Assistant Professor, Materials Science and Engineering
- Member of the Graduate Faculty
- Assistant Professor, Applied Mathematics - GIDP
Contact
- (520) 621-6070
- Mines And Metallurgy, Rm. 141
- Tucson, AZ 85721
- latmarat@arizona.edu
Bio
No activities entered.
Interests
No activities entered.
Courses
2025-26 Courses
-
Dissertation
APPL 920 (Fall 2025) -
Phys-informed machine learning
APPL 527 (Fall 2025) -
Phys-informed machine learning
MSE 527 (Fall 2025)
2024-25 Courses
-
Dissertation
APPL 920 (Spring 2025) -
Dissertation
MSE 920 (Spring 2025) -
Kinetic Process Mat Sci
MSE 572 (Spring 2025) -
Research
MSE 900 (Spring 2025) -
Thesis
MSE 910 (Spring 2025) -
APPL Research
APPL 900 (Fall 2024) -
Dissertation
MSE 920 (Fall 2024) -
Independent Study
MSE 499 (Fall 2024) -
Materials
MSE 595A (Fall 2024) -
Phys-informed machine learning
APPL 527 (Fall 2024) -
Phys-informed machine learning
MSE 527 (Fall 2024) -
Research
MSE 900 (Fall 2024) -
Thesis
MSE 910 (Fall 2024)
2023-24 Courses
-
Internship
MSE 693 (Summer I 2024) -
Dissertation
MSE 920 (Spring 2024) -
Kinetic Process Mat Sci
MSE 572 (Spring 2024) -
Research
MSE 900 (Spring 2024) -
Dissertation
MSE 920 (Fall 2023) -
Internship
MSE 493 (Fall 2023) -
Phys-informed machine learning
APPL 527 (Fall 2023) -
Phys-informed machine learning
MSE 527 (Fall 2023) -
Research
MSE 900 (Fall 2023)
2022-23 Courses
-
Dissertation
MSE 920 (Spring 2023) -
Thermodynamics
MSE 345 (Spring 2023) -
Independent Study
MSE 599 (Fall 2022) -
Research
MSE 900 (Fall 2022) -
Spec Tops Mat Sci+Eng
MSE 596A (Fall 2022)
2021-22 Courses
-
Kinetic Process Mat Sci
MSE 572 (Spring 2022) -
Research
MSE 900 (Spring 2022) -
Research
MSE 900 (Fall 2021)
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.
- Muralidharan, K., Latypov, M., Frantziskonis, G. N., & Nikravesh, Y. (2023). Atomistic characterization of impact bonding in cold spray deposition of copper. Materialia, 26(101736), 11. doi:https://doi.org/10.1016/j.mtla.2023.101736
- 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.
