AI for Science builder | First-principles simulation | Materials informatics | Open-source developer

I am an undergraduate student in Electronic Science and Technology at Sichuan Agricultural University, ranked 2/147 with a 4.22/5.0 GPA. My work sits at the intersection of AI4S, materials science, first-principles simulation, finite-element / electromagnetic simulation, chemical experiments, and AI agents.

I am especially interested in building AI systems that can close the loop between domain knowledge, simulation, experiment, and autonomous discovery.

• Email: zenggaoshan05@gmail.com
• GitHub: github.com/zenggs05
• Research interests: AI4S, AI Scientist agents, ML potentials, materials informatics, DFT/MD, high-entropy materials, microwave absorption, electrochemical sensing, high-throughput screening
• Current direction: reliable AI-assisted workflows for scientific discovery, simulation automation, and developer productivity
• Public writing: WeChat public account 飞鸟想要飞, with 500k+ total views

DFR-related high-throughput screening research
Department of Artificial Intelligence, Xiamen University, 2026.02

• Worked on DFR-related research for accelerating high-throughput screening workflows.
• Focused on connecting AI models with candidate generation, simulation evaluation, and fast property filtering.
• Interested in turning high-throughput screening from a manual pipeline into a more automated and reproducible research loop.

Electrodeposited Cu foils: microstructure and mechanical properties
University of Science and Technology of China / Institute of Metal Research, Chinese Academy of Sciences

• Worked around the system studied in The effect of 2-mercaptobenzimidazole concentration on the microstructure and mechanical properties of electrodeposited Cu foils.
• Studied how 2-mercaptobenzimidazole (MBI) affects electrodeposited Cu foils through grain refinement, surface morphology, crystallographic texture, nanotwinned grains, and thermal stability.
• Followed the electrochemical mechanism behind additive-assisted Cu electrodeposition, including MBI adsorption and complexation with cupric ions.

Machine learning for high-entropy materials
KAUST, visiting student / research assistant, 2025.09 - 2026.03

• Built ML workflows for high-entropy alloys and oxides using ALIGNN, composition-based feature vectors, and JARVIS-DFT data.
• Focused on formation enthalpy, total energy, magnetic moments, and generalization under sparse high-fidelity data.
• Worked on accelerating candidate screening in large composition spaces.

Electronic-structure atlas of MoS2 nanotubes with DeepH and first-principles workflows
Westlake University, research assistant, 2025.07 - 2025.09

• Integrated VASP, OpenMX, ABACUS, Quantum ESPRESSO, CHGNet, and DeepH into automated electronic-structure workflows.
• Studied chirality-dependent bandgaps, effective masses, and direct/indirect bandgap transitions.
• Compared DeepH predictions with DFT-level calculations to evaluate accuracy and computational efficiency.

AI Scientist agents for materials discovery
DeepModeling / AI4S exploration

• Working on agentic workflows for literature understanding, hypothesis generation, simulation planning, and materials-design feedback loops.
• Interested in making scientific agents more reproducible, tool-aware, and grounded in physical constraints.

LLM-guided reverse design of high-entropy microwave absorbers
Manuscript in submission

• Developing an autonomous LLM-agent-guided framework for multi-scale reverse design of high-entropy microwave absorption materials.
• Combining domain reasoning, conditional diffusion / reinforcement-learning style generation, ALIGNN-based stability evaluation, and CST-based electromagnetic simulation.
• Goal: accelerate the discovery of high-performance, ultra-broadband microwave absorbers with physics-aware constraints.

I also build small, focused tools for AI-native development workflows:

• docs-drift-radar: catch stale README snippets, CLI help, and OpenAPI docs in CI.
• pr-evidence-pack: turn a PR diff into a reviewer-ready evidence pack.
• browser-skill-forge: record browser workflows and export Playwright / agent skills.
• Auto_PaperFetcher: automate paper fetching and research-workflow collection.
• vscode-remote-ssh-proxy-codex: improve remote SSH / proxy workflows for coding agents.

Programming and engineering

AI and data science

• Graph neural networks, ALIGNN, ML potentials, diffusion models, reinforcement learning, LSTM/SVR pipelines
• Computer vision for sensing and inspection: U-Net style models, MobileViT-style lightweight models
• Scientific data pipelines: feature engineering, active learning, uncertainty/OOD sampling, benchmark evaluation

Scientific computing and simulation

• First-principles and electronic structure: VASP, CP2K, Quantum ESPRESSO, Materials Studio, OpenMX, ABACUS, DeepH
• Molecular dynamics and chemistry simulation: GROMACS, Gaussian, LAMMPS
• Multiphysics and electromagnetic simulation: COMSOL, CST Studio Suite
• Data analysis and research tooling: Origin, SPSS, Python scientific stack, Linux HPC workflows
• Chemical experiments: materials synthesis, annealing, combustion, hydrothermal preparation, electrochemical sensing, electrode-material preparation

These simulation and experimental skills let me contribute to many published materials papers through DFT modeling, RCS / electromagnetic simulation, COMSOL analysis, Python data processing, and mechanism interpretation.

1. G. Zeng, H. Huang, et al. Machine-Learning and Atomic-Scale Mechanistic Insights for Designing Gradient Porous MOF-Derived Carbon Electrodes. Inorganic Chemistry, 2025.
   First-author work combining ML, DFT/MD, and interpretable design rules for MOF-derived carbon electrodes.

2. N. Wang, X. Kou, G. Zeng, et al. Geometry-defect-spin coupling in chiral high-entropy systems: Multiscale mechanisms of GHz electromagnetic dissipation. Science Advances, 2025.
   Multiscale study of chiral high-entropy systems for broadband electromagnetic dissipation.

• Meritorious Winner, The Mathematical Contest in Modeling
• Third Prize, Asia and Pacific Mathematical Contest in Modeling
• Academic Excellence Scholarship, Sichuan Agricultural University
• 20+ awards across national, provincial, and university-level competitions

I want to keep working on systems that make scientific discovery more programmable:

• AI agents that can read papers, call tools, run simulations, and report evidence.
• ML potential and materials-informatics workflows that scale from small datasets to real discovery loops.
• Open-source developer tools that make research and engineering workflows easier to reproduce.

Outside research, I like long-distance cycling and endurance challenges. I once rode solo for 18 days from Chengdu to Lhasa, covering 2,300+ km across many high-altitude mountain passes. That experience shaped how I think about hard problems: break the route down, keep moving, and do not be afraid of difficult terrain.

For the September 2026 domestic postgraduate recommendation process in China, I hope to find a strong research group where I can keep pushing AI4S, scientific simulation, and autonomous materials discovery forward.