Welcome to the singularity where Catalysts, Microscopy and AI intersect!
袁文浩
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Research paper published on
npj Computational Materials
Generative learning of morphological and contrast heterogeneities for self-supervised electron micrograph segmentation
29
2025-10
Review paper published on
Advanced Materials
Empowering Generalist Material Intelligence with Large Language Models
Featured in Cornell Chronicle
Featured as the Inside Front Cover
12
2025-05
Research Interest
Materials Informatics
Materials Informatics
Machine leaning for computational design of CO2RR catalysts (PhD 's ongoing).
Deep Learning Electron Microscopy
Deep Learning Electron Microscopy
Automated microanalysis for heterogeneous nanocatalyst (master's).
Non-noble Electrocatalysis
Non-noble Electrocatalysis
Deactivation-suppressed electrocatalysts for HER and OER (undergraduate's)
Biography
National University of Singapore
2023 - 2024, M.Eng.
2022 - 2023, Non-Graduating (joint)
Materials Science and Engineering
Hebei University of Technology
2019 - 2023, B.Eng.
Materials Physics
Research Training
2024 I joined the PEESE group under the supervision of Prof. Fengqi You, continuing research on AI methodologies for microscopy and electrocatalysis. The new journey begins and the following work is yet to come...... (Publication [13]-[14])
2022 Senior year, I was selected (out of 9 nationwide) to participate in a joint master's program (two years) at the National University of Singapore, and was fortunate to meet a distinguished microscopist (advisor Prof. Qian He), under whose guidance my research direction took a major shift towards the integration of artificial intelligence methods with scanning transmission electron microscopy, to investigate supported metal catalysts. (Publications [7], [8], and [10]-[12])
2021 Junior year, I joined a hydrogen energy lab (advisor Prof. Hui Liu) and embarked on research on nano-catalytic materials for water electrolysis and fuel cells, which eventually led to my very first step in academics - an first-authored research article published in ACS Appl. Energy Mater (Publication [9]). At the same time, I have also participated as a contributing author in several related projects (Publications [2]-[6]).
2020 Sophomore year, I first started quantum dot-related research in a semiconductor lab as the leader of a undergraduate research team. (Publication [1]).
2019 Freshman year starts with a major in condensed materials physics.
Publications
[14] Yuan, W.; Wang, Z.; You, F.* Quantifying Nanocatalysts from Electron Microscopy with Deep Learning. Submitted.
[13] Yuan, W.; Chen, G.; Wang, Z.; You, F.* Empowering Generalist Material Intelligence with Large Language Models. Adv. Mater. 2025, 37, 2502771. (Link)
[12] Yuan, W.; Yao, B.; Tan, S.; You, F.*; He, Q.* Generative Learning of Morphological and Contrast Heterogeneities for Self-supervised Electron Micrograph Segmentation. npj Comput. Mater. 2025, 11, 322. (Link)
[11] Yuan, W.; Yao, B.; Tan, S.; He, Q.* Generative AI Enables Label-Free Segmentation for Live Analysis of Supported Nanoparticle Catalysts. Microsc. Microanal. 2025, 30, ozae044.211. (M&M 2024 Proceedings, link)
[10] Yuan, W.; Peng, C.; He, Q.* A Large Language Model-Powered Literature Review for High-Angle Annular Dark Field Imaging. Chin. Phys. B 2024, 33, 098703. (invited submission for SPECIAL TOPIC — Stephen J. Pennycook: A research life in atomic-resolution STEM and EELS, link)
[9] Yuan, W.; Li, Y.; Liang, L.; Wang, F.*; Liu, H.* Dual-Anion Doping Enables NiSe2 Electrocatalysts to Accelerate Alkaline Hydrogen Evolution Reaction. ACS Appl. Energy Mater. 2022, 5, 5036-5043. (PDF)
[8] A Sub-nanometer Alloyed Clusters Sustain High Productivity in Propane Dehydrogenation. Research Square preprint 2024 (Link)
[7] Nie, W.; Ren, T.; Zhao, W.; Yao, B.; Yuan, W.; Liu, X.; Abdullah; Zhang, J.; Liu, Q.; Zhang, T.; Tang, S.; He, C.*; Fang, Y.*; Li, X.* Electrochemical Generation of Te Vacancy Pairs in PtTe for Efficient Hydrogen Evolution. ACS Appl. Mater. Interfaces 2024, 16, 21828-21837. (Link)
[6] Wang, F., Zhao, X., Li, Y., Liang, L., Sasaki, K., Hao, Q., Yuan, W., Li, S.* and Liu, H.* Constructing reconstruction-inhibited nickel selenide electrocatalysts via incorporating Ag single atom for durable and efficient water oxidation. Appl. Catal. B: Environ. & Energy 2024, 348, 123830.
[5] Wang, F., Zhang, R., Zhang, Y., Li, Y., Zhang, J., Yuan, W., Liu, H.*, Wang, F.*, Xin, H. L.*, Modulating Electronic Structure of Atomically Dispersed Nickel Sites through Boron and Nitrogen Dual Coordination Boosts Oxygen Reduction. Adv. Funct. Mater. 2023, 33, 2213863. (PDF)
[4] Wang, F.; Yuan, W.; Liang, L.; Li, Y.; Hao, Q.; Chen, C.; Liu, C.*; Liu, H.* Engineering Ni(OH)x/(Ni, Cu)Se2 heterostructure nanosheet arrays for highly-efficient water oxidation. J. Alloys Compd. 2023, 933, 167730. (PDF)
[3] Wang, F.; Zhang, Y.; Yuan, W.; Mao, J.; Wang, K.; Li, Y.; Chen, C.; Liang, L.*, Liu, C.* Plasma Etching of Pyrite-type Nickel Diselenide Nanosheets to Create Selenium Vacancies for Applications as Electrocatalysts for Hydrogen Evolution. ACS Appl. Nano Mater. 2023. 6, 3848–3855. (PDF)
[2] Wang, F.; Zhang, Y.; Zhang, J.; Yuan, W.; Li, Y.; Mao, J.; Liu, C.; Chen, C.; Liu, H.; Zheng, S. In Situ Electrochemically Formed Ag/NiOOH/Ni3S2 Heterostructure Electrocatalysts with Exceptional Performance toward Oxygen Evolution Reaction. ACS Sustain. Chem. Eng. 2022, 10, 5976-5985. (PDF)
[1] Chen, G.*; Du, Q.; Zhang, H.*; Niu, R.; Yuan, W.; Xie, X.; Guo, T.*; Liu, G. Cu-related defects and optical properties in copper–indium–selenide quantum dots by a green synthesis. J. Appl. Phys. 2022. 131, 145704. (PDF)
[Theis] AUTOMATIC ANALYSIS OF STEM IMAGES OF CATALYST SYSTEMS BASED ON DEEP LEARNING MODEL. [Master's Theis, National University of Singapore]. ScholarBank@NUS 2024. (Link)
Presentations
31 July 2024, Cleveland, OH, USA
Poster Presentation
Microscopy and Microanalysis 2024 (M&M 2024)
A09.P2 - Automation in Microscopy from Image Acquisition to Image Analysis, Data Visualization, and Management
Generative AI Enables Label-free Segmentation for Live Analysis of Supported Nanoparticle Catalysts
16 January 2024, Singapore
Poster Presentation
Inaugural Symposium of the NUS Electron Microscopy Facility
Leverage virtual data to train segmentation network for identifying nanoparticles on STEM images
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