Skip to content

Latest commit

 

History

History
79 lines (57 loc) · 4.51 KB

about.md

File metadata and controls

79 lines (57 loc) · 4.51 KB
permalink title excerpt author_profile redirect_from
/
true
/about/
/about.html

{% if site.google_scholar_stats_use_cdn %} {% assign gsDataBaseUrl = "https://cdn.jsdelivr.net/gh/" | append: site.repository | append: "@" %} {% else %} {% assign gsDataBaseUrl = "https://raw.githubusercontent.com/" | append: site.repository | append: "/" %} {% endif %} {% assign url = gsDataBaseUrl | append: "google-scholar-stats/gs_data_shieldsio.json" %}

Hi! I'm Mouyang Cheng (程谋阳), 1st year graduate student in MIT CSE-DMSE (Computational Science and Engineering @ Department of Material Science and Engineering) program. I got my bachelor's degree in Peking University, China, majoring in physics, where I conducted my undergraduate research in Prof. Ji Chen's group. For the PhD career I'm now doing research at Prof. Mingda Li's group. My research interest covers a wide range of topics in computational material science, closely bonded to understanding actual experiments.

Specifically, my current interest includes understanding defects and amorphous systems using computational methods, and predicting experimental synthesis recipe using advanced machine learning techniques. I'm also generally interested in predicting novel properties of energy materials, and inverse designing next-generation advanced functional materials.

There will be future updates on this personal page! Thank you for your visiting!

📝 Selected publications

For full list of publications please refer to my Google scholar page.

Arxiv
sym

AI-driven materials design: a mini-review

Mouyang Cheng* †, Chu-Liang Fu†, Ryotaro Okabe†, Abhijatmedhi Chotrattanapituk† et al. (†Equal contribution.)

  • Review article on AI-driven materials design.
  • Highlighting a shift of paradigm from forward screening to inverse design, driven by deep generative models.
Matter
sym

Machine learning detection of Majorana zero modes from zero-bias peak measurements

Mouyang Cheng*, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Mingda Li*

  • Majorana zero modes (MZMs) show promise for topological quantum computation via zero-bias peaks (ZBPs)
  • Machine learning framework distinguishes MZM signals from spurious ZBP signals with up to 94% accuracy.
Nature
sym

Disorder tuned conductivity in amorphous monolayer carbon

Huifeng Tian†, Yinhang Ma†, Zhenjiang Li†, Mouyang Cheng†, Shoucong Ning† et al. (†Equal contribution.)

  • Medium range order and density of crystalline sites tune electric conductivity to up to 9 orders of magnitude.
  • Unravelling the complex structure-property relation of amorphous monolayer carbon.

🎖 Honors and Awards

  • 2024.03 Excellent Undergraduate Research Project, Peking University (Undergraduate)
  • 2023.09 National Scholarship, Peking University (Undergraduate, top 1%)
  • 2022.09 National Scholarship, Peking University (Undergraduate, top 1%)
  • 2021.09 National Scholarship, Peking University (Undergraduate, top 1%)

🔥 News

  • 2025.02: I uploaded my first review paper “AI-driven materials design: a mini-review” as lead author!
  • 2024.09: I joined Mingda's research group!
  • 2024.07: “Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements” has been accepted by Matter and selected as the highlighted article for 5 $^{\text{th}}$ anniversary of the journal.

📖 Educations

  • 2024.07 - (now), Ph.D. Computational Science and Engineering, MIT, US
  • 2020.09 - 2024.07, B.S. Physics, Peking University, China