SCIENTIFIC INTELLIGENCE · CRYSTAL STRUCTURES · MATERIALS DISCOVERY
An intelligent scientific agent for X-ray diffraction.
Connecting specialized AI, crystallographic knowledge, and professional analysis tools.
Project Intro ↗ / Paper ↗ / Repositories ↗ / Our Team ↗ / 小红书 ↗
The Platform Understand the structure behind the pattern. Gan Jiang brings the XRD analysis workflow into one coordinated framework. It integrates specialized AI models, crystallographic databases, and professional tools to support phase identification, structural refinement, and evidence-based interpretation. From a single phase to a complex mixture, the platform connects analytical steps to the diffraction evidence—helping researchers move from measured patterns to supported structural conclusions.
Capabilities
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01 / PREPARE
Process diffraction patterns, convert data formats, and detect peaks for subsequent analysis. |
02 / IDENTIFY
Analyse single-phase and multiphase patterns using AI models and crystallographic reference databases. |
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03 / REFINE
Connect candidate crystal structures to professional refinement tools and assess their agreement with measured data. |
04 / INTERPRET
Coordinate analysis tools and interpret results in the context of diffraction evidence and crystallographic constraints. |
The Workflow
Diffraction data → Phase identification → Structural refinement → Interpretation
Specialized tools, coordinated by a scientific agent.
Research & Ecosystem This GitHub organization hosts selected open-source components, research tools, technical resources, and ecosystem projects for academic research and community collaboration. Gan Jiang is a commercial scientific platform. Its core system and proprietary technologies are not fully open-sourced. Please refer to each repository for its scope, documentation, and license.
Discover the platform and follow our latest developments.
Visit the official website ↗ · 小红书 · 95614037352 ↗
GAN JIANG
Advancing materials discovery through intelligent diffraction analysis.
If you use Gan Jiang in your research, please cite our paper:
@misc{cao2026selflearningscientificagentxray,
title = {A self-learning scientific agent for X-ray diffraction},
author = {Bin Cao and Huichi Zhou and Runyu Yang and Jingsong Li and Shuchen Sun and Yan Song and Hanyu Gao and Zhongwei Yu and Tong-Yi Zhang and Jun Wang},
year = {2026},
eprint = {2610.07862},
archivePrefix = {arXiv},
primaryClass = {cond-mat.mtrl-sci},
url = {https://arxiv.org/abs/2610.07862}
}