Zhenze Yang

Zhenze Yang

PhD student

MIT

Biography

I am currently a PhD student in DMSE of MIT and I work in Laboratory for Atomistic and Molecular Mechanics (LAMM). My research interests include combining machine learning and deep learning techniques with multiscale simulation methods (FEM,MD,DFT) to accelerate the property calculations and designs of diverse materials such as composites, nanomaterials and biological materials. Before that, I got my bachelor degree in Physics from University of Chinese Academy of Sciences (UCAS). I was also an intern at Toyota Research Institute (TRI), working on AI for battery materials.

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Interests
  • Computational Materials Science
  • Computational mechanics
  • Artificial Intelligence
Education
  • PhD in Materials Science and Engineering, 2024

    MIT

  • BSc in Physics, 2019

    University of Chinese Academy of Sciences

Experience

 
 
 
 
 
Toyota Research Institute
Summer intern
Toyota Research Institute
May 2023 – Aug 2023
Working on AI for battery electrolyte materials.
 
 
 
 
 
MIT
Graduate Research Assistant
MIT
Sep 2019 – Present
Interdisciplinary research using ML for materials science.
 
 
 
 
 
UC Berkeley
Undergraduate Research Assistant
UC Berkeley
Sep 2018 – Feb 2019
Modeling research of chromatin folding.
 
 
 
 
 
MIT
Undergraduate Research Assistant
MIT
Jun 2018 – Sep 2018
Optimization development of polymer assemblies.
 
 
 
 
 
Institute of Physics, CAS
Undergraduate Research Assistant
Institute of Physics, CAS
Sep 2017 – Jun 2019
Computational and experimental research for water droplet wetting and selective transport via nuclear pore complex.
 
 
 
 
 
Technical Institute of Physics and Chemistry, CAS
Undergraduate Research Assistant
Technical Institute of Physics and Chemistry, CAS
Jul 2017 – Oct 2017
Experimental research on liquid metal droplets.

Projects

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Publications

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(2024). De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models. In An MIT Exploration of Generative AI.

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(2024). De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models. In ArXiv.

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(2024). A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model. In ArXiv.

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(2022). Fracture at the two-dimensional limit. In MRS Bull. (Review paper).

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(2022). Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning. In Adv. Theory Simul..

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(2022). Water contact angles on charged surfaces in aerosols. In Chin. Phys. B.

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(2021). Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning. In JACS Au.

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(2021). Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks. In Front. Mater. (Featured by MIT CEE news).

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(2021). Deep learning model to predict complex stress and strain fields in hierarchical composites. In Sci. Adv. (Featured by MIT News, EurekAlert!, Phys.org, ScienceDaily and SciTechDaily).

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(2020). Artificial Intelligence and Machine Learning in Mechanical Design of Materials. In Mater. Horiz. (Review paper).

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(2018). Liquid Metal Corrosion Effects on Conventional Metallic Alloys Exposed to Eutectic Gallium–Indium Alloy Under Various Temperature States. In Int. J. Thermophys..

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(2018). Metallic Bond Enabled Wetting Behavior at the Liquid Ga/CuGa2 Interfaces. In ACS Appl. Mater. Interfaces.

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