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Explore our research on the densification process and microstructural evolution of AlN nanoceramics during hot-press sintering, where molecular dynamics simulations reveal the intricate effects of temperature, pressure, and particle size
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Explore the distinct microstructural responses under compression in FeCoCrNi high entropy alloys
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A computational approach for SFE estimation
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A computational method to unravel material energy landscapes
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Exploring the transition from FCC to amorphous phases in high entropy alloys with varying atomic size differences
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Unraveling the complex interplay between colloids and nanoporous media using Dissipative Particle Dynamics (DPD)
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🏆 Second Prize Winner – AI 4 Science Challenge (NanoHub)
Published in Coatings, 2018
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Recommended citation: Liang, A., Jiang, X., Hong, X., Jiang, Y., Shao, Z., & Zhu, D. (2018). "Recent developments concerning the dispersion methods and mechanisms of graphene." Coatings, 8(1), 33. https://doi.org/10.3390/coatings8010033
Published in Physical Review Materials, 2021
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Recommended citation: Liang, A., Liu, C., & Branicio, P. S. (2021). "Hot-press sintering of aluminum nitride nanoceramics." Physical Review Materials, 5(9), 096001. https://doi.org/10.1103/PhysRevMaterials.5.096001
Published in Computational Materials Science, 2022
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Recommended citation: Guan, X., Liang, A., & Branicio, P. S. (2022). "High pressure shear induced microstructural evolution in nanocrystalline aluminum." Computational Materials Science, 203(15), 111105. https://doi.org/10.1016/j.commatsci.2021.111105
Published in Materials & Design, 2023
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Recommended citation: Yuan, S., Liang, A., Liu, C., Nakano, A., Nomura, K., & Branicio, P. S. (2023). "Uncovering hidden vacancy-like motifs in metallic glasses with machine learning." Materials & Design, 233, 112185. https://doi.org/10.1016/j.matdes.2023.112185
Published in Acta Materialia, 2023
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Recommended citation: Liang, A., Goodelman, D. C., Hodge, A. M., Farkas, D., & Branicio, P. S. (2023). "CoFeNiTix and CrFeNiTix high entropy alloy thin films microstructure formation." Acta Materialia, 257, 119163. https://doi.org/10.1016/j.actamat.2023.119163
Published in Physical Review Materials, 2023
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Recommended citation: Yuan, S., Liang, A., Liu, C., Tian, L., Mousseau, N., & Branicio, P. S. (2023). "Effect of heat treatment paths on the aging and rejuvenation of metallic glasses." Physical Review Materials, 7(12), 123603. https://doi.org/10.1103/PhysRevMaterials.7.123603
Published in Acta Materialia, 2024
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Recommended citation: Alwen, A., Liang, A., Branicio, P. S., & Hodge, A. M. (2024). "Combinatorial and high-throughput investigation of growth nanotwin formation." Acta Materialia, 270, 119839. https://doi.org/10.1016/j.actamat.2024.119839
Published in Langmuir, 2024
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Recommended citation: Liang, A., Liu, C., & Branicio, P. S. (2024). "Colloid Transport in Bicontinuous Nanoporous Media." Langmuir, 40(21), 10868. https://doi.org/10.1021/acs.langmuir.4c00037
Published in Journal of Materials Research and Technology, 2025
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Recommended citation: Carvalho, A. P., Liang, A., Kawasaki, Cupertino-Malheiros, L., Branicio, P. S., & Figueiredo, R. B. (2025) "Strengthening nanostructured metals through dynamic recovery." Journal of Materials Research and Technology, 35, 754. https://doi.org/10.1016/j.jmrt.2025.01.053
Published in Journal of Materials Research and Technology, 2025
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Recommended citation: Bulatov, V. V., Bertin, N., Aubry, S., Zepeda-Ruiz, L. A., Zhou, X., Liang, A., Oppelstrup, T., Sadig, B. (2025) "Network aspects of single crystal plasticity." Journal of Materials Research and Technology, 36, 5611. https://doi.org/10.1016/j.jmrt.2025.04.014
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Poster for 2021 MFD Student Research Symposium
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Best poster Nominee! 
Poster for 2021 MRS Fall Conference
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Poster for 2022 TMS Annual Meeting & Exhibition
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Poster for 2022 MFD Student Research Symposium
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Poster for 2023 TMS Annual Meeting & Exhibition
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Poster for 2023 MFD Student Research Symposium
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Aoyan Triumphs with Best Presentation Award 🥳 
Graduate Teaching Assistant, University of Southern California, Mork Family Department of Chemical Engineering & Materials Science, 2022
Instructor: Dr. Ken-ichi Nomura
Course Summary: This course introduces basics of atomistic simulation methods with special focuses on Molecular Dynamics (MD) and Monte Carlo (MC) simulations. Due to the rapid advances of high-performance parallel computers, atomistic simulation has become an essential tool to investigate material structures and properties. The course provides overview of atomistic simulations, their applications, underlying theory and algorithms. Hands-on projects using the USC Advanced Research Computing supercomputer develops solid understanding of how to design and perform materials simulations. This course is designed for graduate students with any science and engineering background. No prior experience of programming is necessary.
Graduate Teaching Assistant, University of Southern California, Mork Family Department of Chemical Engineering & Materials Science, 2022
Instructor: Dr. Ken-ichi Nomura & Dr. Lessa Grunenfelder
Course Summary: This is an introductory course intended for undergraduate engineering students. Key concepts in chemistry are discussed in the context of materials science and engineering applications. The laboratory component of the course provides students with hands-on experience, reinforcing concepts covered in lecture through direct observation and experimentation. Topics include the electronic structure of atoms, elements and the periodic table, organic and inorganic compounds, chemical reactions, kinetics and thermodynamics, and the structure and properties of engineering materials.
Graduate Teaching Assistant, University of Southern California, Mork Family Department of Chemical Engineering & Materials Science, 2023
Instructor: Prof. Paulo Branicio
Course Summary: This is a foundational mathematical course for deep learning. It provides graduate students with in-depth knowledge of mathematics needed to understand deep learning. The course covers a variety of topics such as linear algebra, probability and statistics, optimization, Fourier series, Fourier transforms, ordinary and partial differential equations, and Markov Chain Monte Carlo methods. Each topic is introduced with an application of deep learning to problems in the physical sciences and engineering. Students are required to do five projects chosen from the following topics: feed-forward neural network, convolutional neural network, recurrent neural network, neural network solvers for differential equations, autoencoders, restricted Boltzmann machine and deep Boltzmann machine.
Graduate Teaching Assistant, University of Southern California, Mork Family Department of Chemical Engineering & Materials Science, 2023
Instructor: Prof. Paulo Branicio
Course Summary: This course is intended for graduate students in Materials Science and Engineering. The course aims to introduce students to a broad treatment of classical and statistical thermodynamics and its applications to equilibrium properties of materials. The course will provide a thermodynamic framework for treating general phenomena in materials science, e.g., chemical reactions, diffusion, and point defects. A focus of the course will be maps of equilibrium states such as phase diagrams. Course topics include the laws of thermodynamics, statistical thermodynamics, solutions, phase equilibria, phase transformations, and phase diagrams of binary and ternary alloys.
Graduate Teaching Assistant, University of Southern California, Mork Family Department of Chemical Engineering & Materials Science, 2024
Instructor: Dr. Eyal Ben-Yoseph
Course Summary: This course reviews the production processes of confectionery products, starting with simple sweets such as hard candies and sugar shell, through jelly, marshmallow, nougat, and toffee, to the more complex systems of caramel, fudge, and chocolate. While learning about the equipment and technology, we cover the chemical and physical mechanisms that give our candies their taste, texture, and appearance. This includes solubility, nucleation, crystals growth, the amorphous state, phase transitions, heat and mass transfer, vapor pressure, emulsions and foams stability, browning, gelation, polymorphism, and more. We will also describe how process and product development is done in R&D through the stage-gate framework, and how data science tools accelerate development and increase reliability
