Computational Physics
Ab initio calculations like DFT, BSE-GW & AIMD. Especially in improving theoretical calculations of core-loss spectra
Institute of Industrial Science · The University of Tokyo
Yinan Wang
Materials science & measurement informatics
I am in the integrated Master's and Ph.D. program at the University of Tokyo. My research combines first-principles calculations and machine learning to understand core-loss spectra and two-dimensional ferroelectric materials.
News
- Our new paper on region-resolved machine learning of XANES has been selected for the Inside Front Cover of Digital Discovery! Read paper · RSC
- I'm going to give a poster presentation at The 62nd Symposium on X-Ray Analysis @ Kunibiki Messe, Shimane Prefectural Convention Center
- I'm going to give a poster presentation at ACSIN-16 & ICSPM34 @ Arcrea HIMEJI
- Our manuscript is accepted by Digital Discovery!
- I have given an oral presentation at The 87th JSAP Autumn Meeting, Hokkaido University.
- We have submitted our new manuscript on Arxiv. arXiv:2608.09216
- I'm going to give an oral presentation at The 82nd Annual Meeting of The Japanese Society of Microscopy @ Sendai International Center.
- We have submitted our new manuscript on Arxiv. arXiv:2604.27342
- We have submitted our new manuscript on Arxiv. Read paper · RSC *Accepted by Digital Discovery
- I updated my personal cv. View CV
- I'm going to give an oral presentation at The 73rd JSAP Spring Meeting @ Institute of Science Tokyo.
- Our paper is published by Ultramicroscopy! Read paper
- We submitted our new paper.
- Presented at 28th XAFS annual meeting @KEK
Research interests
Explore my researchX-ray absorption spectroscopy (XAS), electron energy-loss spectroscopy (EELS), and atomistic simulation.
Data-Driven Analysis
Spectroscopic analysis methodology based on machine learning and artificial intelligence.
Measurement Informatics
Developing and applying informatics to accelerate scientific discovery by systematically integrating and analyzing complex experimental data.
Machine Learning Force Field (MLFF)
Development of generalizable MLFF, utilizing on-the-fly training strategies to achieve efficient reproduction of ab initio calculations.
2D materials
Manipulating electronic states and dielectric response in 2D materials through atomic-scale doping and First-Principles modeling
Education
- 2025.04 — Present
- The University of TokyoDepartment of Materials Engineering
- 2021.04 — 2025.03
- Tokyo University of ScienceDepartment of Materials Science and Technology
- 2017.09 — 2020.07
- High School Affiliated to Fudan University
Honors & fellowships
Languages & skills
Language
Mandarin (Native)
English (TOEFL 100)
Japanese (JLPT N1)
Programming
Python
Fortran (In progress)
Ab Initio Calculations
VASP (DFT, AIMD)
CASTEP
FEFF9
FDMNES
Membership
- The Japan Society of Applied Physics (JSAP)
- The Japanese XAFS Society (XSJ)
- The Japanese Society for Synchrotron Radiation Research (JSR)
- The Japanese Society of Microscopy (JSM)
- Materials Research Society (MRS)
