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 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).
Download my resumé.
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
Graduate Research Assistant
MIT
Interdisciplinary research using ML for materials science.
Undergraduate Research Assistant
UC Berkeley
Modeling research of chromatin folding.
Undergraduate Research Assistant
MIT
Optimization development of polymer assemblies.
Undergraduate Research Assistant
Institute of Physics, CAS
Computational and experimental research for water droplet wetting and selective transport via nuclear pore complex.
Undergraduate Research Assistant
Technical Institute of Physics and Chemistry, CAS
Experimental research on liquid metal droplets.
Projects
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Recent & Upcoming Talks
Publications
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(2023).
Fill in the Blank: Transferrable Deep Learning Approaches to Recover Missing Physical Field Information.
In Adv. Mater..
(2023).
Generative multiscale analysis of de novo proteome-inspired molecular structures and nanomechanical optimization using a VoxelPerceiver transformer model.
In JMPS.
(2022).
Fracture at the two-dimensional limit.
In MRS Bull. (Review paper).
(2022).
Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks.
In Npj Comput. Mater..
(2022).
Hierarchical Multiresolution Design of Bioinspired Structural Composites Using Progressive Reinforcement Learning.
In Adv. Theory Simul..
(2022).
Rapid mechanical property prediction and de novo design of three-dimensional spider webs through graph and GraphPerceiver neural networks.
In JAP (Featured by Scilight).
(2022).
High-Throughput Generation of 3D Graphene Metamaterials and Property Quantification Using Machine Learning.
In Small Methods.
(2022).
End-to-End Deep Learning Approach to Predict Complex Stress and Strain Fields Directly from Microstructural Images.
In US Patent App.
(2022).
Generative design, manufacturing, and molecular modeling of 3D architected materials based on natural language input.
In APL Mater.
(2022).
Water contact angles on charged surfaces in aerosols.
In Chin. Phys. B.
(2021).
Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning.
In JACS Au.
(2021).
Words to Matter: De novo Architected Materials Design Using Transformer Neural Networks.
In Front. Mater. (Featured by MIT CEE news).
(2021).
End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures.
In JMPS.
(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).
(2020).
Artificial Intelligence and Machine Learning in Mechanical Design of Materials.
In Mater. Horiz. (Review paper).
(2018).
Liquid Metal Corrosion Effects on Conventional Metallic Alloys Exposed to Eutectic Gallium–Indium Alloy Under Various Temperature States.
In Int. J. Thermophys..
(2018).
Metallic Bond Enabled Wetting Behavior at the Liquid Ga/CuGa2 Interfaces.
In ACS Appl. Mater. Interfaces.