Portrait of Chi Zhihe 池之荷

Researcher profile

Chi Zhihe 池之荷

Postdoctoral Researcher · 博士后

School of Biomedical Sciences and Engineering, Guangzhou International Campus, South China University of Technology

I explore AI-driven materials research, applying machine-learning methods to study structure–property relationships and design organic luminescent and functional materials.

  • AI for Materials Science
  • Mechanism Discovery and Rule Learning
  • Organic Luminescent and Functional Materials
Email Chi Zhihe 池之荷

About

About Chi Zhihe 池之荷

I am a postdoctoral researcher interested in AI for Materials Science. My doctoral research focused on polymer-based room-temperature phosphorescent materials, including molecular design, material fabrication, and luminescence-property regulation. I am now exploring how machine-learning methods can be used to organize data, identify structure–property relationships, and support the design of organic luminescent and functional materials. I am particularly interested in building models that are not only predictive but also scientifically interpretable, so that they can help reveal meaningful material-design principles.

Selected publications

  1. Chi, Z.; Yang, J.; Li, Z. Natural polymer-based room-temperature phosphorescent materials. Cell Reports Physical Science 6, 102673 (2025).
  2. Chi, Z.; Li, D.; Meng, Y.; Qiao, Z.; Yang, J.; Zhou, J.; Liu, X.; Li, Z. Cross-linked polymer phosphorescence fibers based on chitosan and corresponding wet-spinning processing. Science China Chemistry 69, 413–419 (2026).

Research questions

Questions I care about

  1. How can invariant learning and constraint-based modeling extract robust structural patterns from complex experimental and simulation data?

  2. How can mechanism discovery and rule learning move materials modeling beyond data-driven prediction toward mechanistic understanding?

  3. How can physically and chemically consistent interpretable latent-variable models reveal structure–property relationships in materials?

  4. How can causal modeling identify key controllable variables and causal pathways for the design of organic luminescent and functional materials?

Current work

What I am working on

Initial-stage project development

Data-Driven Design of Ultralong Organic Room-Temperature Phosphorescent Materials

This work focuses on the curation and standardization of structure–condition–property data, mechanism-relevant feature analysis, and machine-learning prediction for ultralong organic room-temperature phosphorescent materials. The goal is to use AI to uncover materials-design principles and support the development of new materials.

Contribution profile

What I bring to a project

Methods

  • Molecular design, synthesis, and fabrication of luminescent materials
  • Characterization and analysis of photophysical properties, including phosphorescence emission, lifetime, and quantum yield

Scientific domains

  • Organic and polymer-based room-temperature phosphorescent materials

I can contribute

  • Analyzing the performance of organic materials from the perspectives of molecular structure, material morphology, and environmental factors