diff --git a/MindChem/LICENSE b/MindChem/LICENSE
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--- /dev/null
+++ b/MindChem/LICENSE
@@ -0,0 +1,202 @@
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diff --git a/MindChem/NOTICE b/MindChem/NOTICE
new file mode 100644
index 0000000000000000000000000000000000000000..7424fa0007a2fcf6608f76802045c8fc4875914e
--- /dev/null
+++ b/MindChem/NOTICE
@@ -0,0 +1,2 @@
+MindSpore MindChemistry
+Copyright 2019-2025 Huawei Technologies Co., Ltd
diff --git a/MindChem/OWNERS b/MindChem/OWNERS
new file mode 100644
index 0000000000000000000000000000000000000000..64c40ad46b07fcbc342ef45053b90be5752f9c2d
--- /dev/null
+++ b/MindChem/OWNERS
@@ -0,0 +1,5 @@
+approvers:
+- birfied
+
+reviewers:
+- birfied
diff --git a/MindChem/README.md b/MindChem/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..5b7b956b7ea0cdc6b1d8b5ad7eb9e53e32277bed
--- /dev/null
+++ b/MindChem/README.md
@@ -0,0 +1,98 @@
+# MindSpore Chemistry
+
+[查看中文](README_CN.md)
+
+[](https://badge.fury.io/py/mindspore)
+[](https://github.com/mindspore-ai/mindspore/blob/master/LICENSE)
+[](https://gitee.com/mindspore/mindscience/pulls)
+
+---
+
+## Contents
+
+- [MindSpore Chemistry](#mindspore-chemistry)
+ - [Contents](#contents)
+ - [Introduction](#introduction)
+ - [Latest News](#latest-news)
+ - [Models & Applications](#models--applications)
+ - [Machine Learning Force Fields](#machine-learning-force-fields)
+ - [Property Prediction](#property-prediction)
+ - [Structure Generation](#structure-generation)
+ - [Community](#community)
+ - [Core Contributors](#core-contributors)
+ - [Contribution Guide](#contribution-guide)
+ - [License](#license)
+ - [References](#references)
+
+---
+
+## Introduction
+
+Conventional chemistry studies have long been confronted with numerous challenges. The process of experimental design, synthesis, characterization, and analysis can be time-consuming, costly, and highly dependent on experts’ experiences.
+The synergy between AI and chemistry offers unprecedented opportunities to overcome the limitations of conventional approaches and unlock new frontiers in scientific discovery and innovation. AI techniques can efficiently process vast amount of data, mining underneath patterns and generating predictive models. By leveraging AI, chemistry and material science researchers can accelerate the design and optimization of chemical processes and the design and analysis of novel materials.
+
+**MindSpore Chemistry**(MindChemistry) is a toolkit built on MindSpore endeavoring to integrate AI with conventional chemistry research. It supports multi-scale tasks including molecular generation, property prediction and synthesis optimization on multiple chemistry systems such as organic, inorganic and composites chemistry systems. MindChemistry dedicates to enabling the joint research of AI and chemistry with high efficiency, and seek to facilitate an innovative paradigm of joint research between AI and chemistry, providing experts with novel perspectives and efficient tools.
+
+
+
+## Latest News
+
+- `2025.07.07` Added Orb model support.
+- `2025.04.16` Added CrystalFlow model support.
+- `2025.03.30` MindChemistry 0.2.0 has been released, featuring several applications including NequIP, DeephE3nn, Matformer, and DiffCSP.
+- `2024.07.30` MindChemistry 0.1.0 has been released.
+
+## Models & Applications
+
+---
+
+### Machine Learning Force Fields
+
+| Model | System | Dataset | Task |
+|-------|--------|---------|------|
+| [NequIP](./applications/nequip/) | Small molecules | Revised Molecular Dynamics 17 (rMD17) dataset | Molecular energy prediction using E(3)-equivariant GNNs |
+| [Orb](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/orb) | Molecular and crystalline materials | Large-scale 3D atomic-structure datasets; DFT calculations | General GNN interatomic potential for energy, forces, and stress; suitable for molecular dynamics simulation |
+
+### Property Prediction
+
+| Model | System | Dataset | Task |
+|-------|--------|---------|------|
+| [DeephE3nn](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/deephe3nn) | Materials systems | Bilayer graphene dataset | E(3)-equivariant neural network for electronic Hamiltonian prediction |
+| [Matformer](./applications/matformer/) | Crystalline materials | JARVIS-DFT 3D dataset | Graph + Transformer for materials property prediction |
+
+### Structure Generation
+
+| Model | System | Dataset | Task |
+|-------|--------|---------|------|
+| [DiffCSP](./applications/diffcsp/) | Crystalline materials | Stable crystal structure datasets (MP-20, MPTS-52, Carbon-24) | Crystal structure prediction/generation via joint diffusion |
+| [CrystalFlow](./applications/crystalflow/) | Crystalline materials | Materials database crystal structure datasets (MP-20, Carbon-24, MPTS-52) | Flow-based crystal structure generation |
+
+## Community
+
+### Core Contributors
+
+Thanks goes to these wonderful people:
+
+Danyang Chen, Jianhuan Cen, Kunming Xu, wujian, wangyuheng, Lin Peijia, gengchenhua, caowenbin，Siyu Yang
+
+## Contribution Guide
+
+- Please click here to see how to contribute your code:[Contribution Guide](https://gitee.com/mindspore/mindscience/blob/master/CONTRIBUTION.md)
+
+## License
+
+[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+## References
+
+[1] Batzner S, Musaelian A, Sun L, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials[J]. Nature Communications, 2022, 13(1): 2453.
+
+[2] Neumann M, Gin J, Rhodes B, Bennett S, Li Z, Choubisa H, Hussey A, Godwin J. Orb: A Fast, Scalable Neural Network Potential[J]. arXiv:2410.22570, 2024.
+
+[3] Xiaoxun Gong, He Li, Nianlong Zou, et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian[J]. Nature Communications, 2023, 14: 2848.
+
+[4] Keqiang Yan, Yi Liu, Yuchao Lin, Shuiwang Ji, et al. Periodic Graph Transformers for Crystal Material Property Prediction[J]. arXiv:2209.11807v1 [cs.LG], 2022.
+
+[5] Jiao Rui, Huang Wenbing, Lin Peijia, et al. Crystal structure prediction by joint equivariant diffusion[J]. Advances in Neural Information Processing Systems, 2024, 36.
+
+[6] Luo X, Wang Z, Wang Q, et al. CrystalFlow: a flow-based generative model for crystalline materials[J]. Nature Communications, 2025, 16: 9267.
\ No newline at end of file
diff --git a/MindChem/README_CN.md b/MindChem/README_CN.md
new file mode 100644
index 0000000000000000000000000000000000000000..96d610b9770ca40be06ce5a2535027cc2ef4145c
--- /dev/null
+++ b/MindChem/README_CN.md
@@ -0,0 +1,106 @@
+# MindSpore Chemistry
+
+[View English](README.md)
+
+[](https://badge.fury.io/py/mindspore)
+[](https://github.com/mindspore-ai/mindspore/blob/master/LICENSE)
+[](https://gitee.com/mindspore/mindscience/pulls)
+
+---
+
+## 目录
+
+- [MindSpore Chemistry](#mindspore-chemistry)
+ - [目录](#目录)
+ - [介绍](#介绍)
+ - [最新消息](#最新消息)
+ - [模型与应用](#模型与应用)
+ - [机器学习力场](#机器学习力场)
+ - [性质预测](#性质预测)
+ - [结构生成](#结构生成)
+ - [社区](#社区)
+ - [核心贡献者](#核心贡献者)
+ - [贡献指南](#贡献指南)
+ - [许可证](#许可证)
+ - [引用](#引用)
+
+---
+
+## 介绍
+
+传统化学研究长期以来面临着众多挑战，实验设计、合成、表征和分析的过程往往耗时、昂贵，并且高度依赖专家经验。AI与化学的协同可以克服传统方法的局限性、开拓全新的研究范式，结合AI模型与化学知识，可以高效处理大量数据、挖掘隐藏的关联信息，构建仿真模型，从而加快化学反应的设计和优化，实现材料的性质预测，并辅助设计新材料。
+
+**MindSpore Chemistry**（MindChemistry）是基于 MindSpore 与 MindScience 构建的化学领域套件，支持多体系（有机/无机/复合材料化学）、多尺度任务（微观分子生成/预测、宏观反应优化）的AI+化学仿真，致力于高效使能AI与化学的融合研究，践行和牵引AI与化学联合多研究范式跃迁，为化学领域专家的研究提供全新视角与高效的工具。
+
+
+
+---
+
+## 最新消息
+
+- `2025.04.16` 增加CrystalFlow模型支持；
+- `2025.07.07` 增加Orb模型支持；
+- `2025.03.30` MindChemistry 0.2.0版本发布，包括多个应用案例，支持NequIP、DeephE3nn、Matformer以及DiffCSP模型；
+- `2024.07.30` MindChemistry 0.1.0版本发布；
+
+---
+
+## 模型与应用
+
+下面是当前支持的主要模型及其用途概览，便于快速了解与定位示例：
+
+---
+
+### 机器学习力场
+
+| 模型 | 体系 | 数据 | 任务 |
+|---------|------|------|------|
+| [NequIP](./applications/nequip/) | 小分子 | Revised Molecular Dynamics 17 (rMD17) 数据集 | 分子能量预测，基于等变计算与图神经网络 |
+| [Orb](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/orb) | 分子与晶体材料体系 | 大规模三维原子结构数据集，DFT 计算结果 | 通用图神经网络势，预测能量、力、应力，用于分子动力学模拟等 |
+
+### 性质预测
+
+| 模型 | 体系 | 数据 | 任务 |
+|---------|------|------|------|
+| [DeephE3nn](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/deephe3nn) | 材料体系 | 双层石墨烯数据集 | 基于 E(3)-等变神经网络预测电子哈密顿量 |
+| [Matformer](./applications/matformer/) | 晶体材料体系 | JARVIS-DFT 3D数据集 | 基于图神经网络 + Transformer 预测材料性质 |
+
+### 结构生成
+
+| 模型 | 体系 | 数据 | 任务 |
+|---------|------|------|------|
+| [DiffCSP](./applications/diffcsp/) | 晶体材料体系 | 稳定晶体结构数据集（MP-20、MPTS-52、Carbon-24等） | 基于联合扩散的晶体结构预测/生成 |
+| [CrystalFlow](./applications/crystalflow/) | 晶体材料体系 | 材料数据库晶体结构数据集（MP-20、Carbon-24、MPTS-52等） | 基于归一化流的晶体结构生成 |
+
+## 社区
+
+### 核心贡献者
+
+感谢以下开发者做出的贡献：
+
+Danyang Chen, Jianhuan Cen, Kunming Xu, wujian, wangyuheng, Lin Peijia, gengchenhua, caowenbin，Siyu Yang
+
+## 贡献指南
+
+- 如何贡献您的代码，请点击此处查看：[贡献指南](https://gitee.com/mindspore/mindscience/blob/master/CONTRIBUTION.md)
+
+## 许可证
+
+[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+## 引用
+
+[1] Batzner S, Musaelian A, Sun L, et al. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials[J]. Nature communications, 2022, 13(1): 2453.
+
+[2] Neumann M, Gin J, Rhodes B, Bennett S, Li Z, Choubisa H, Hussey A, Godwin J. Orb: A Fast, Scalable Neural Network Potential[J]. arXiv:2410.22570, 2024.
+
+[3] Xiaoxun Gong, He Li, Nianlong Zou, et al. General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian[J]. Nature communications, 2023, 14: 2848.
+
+[4] Keqiang Yan, Yi Liu, Yuchao Lin, Shuiwang ji, et al. Periodic Graph Transformers for Crystal Material Property Prediction[J]. arXiv:2209.11807v1 [cs.LG] 23 sep 2022.
+
+[5] Jiao Rui and Huang Wenbing and Lin Peijia, et al. Crystal structure prediction by joint equivariant diffusion[J]. Advances in Neural Information Processing Systems, 2024, 36.
+
+[6] Luo X, Wang Z, Wang Q, et al. CrystalFlow: a flow-based generative model for crystalline materials[J]. Nature communications, 2025, 16: 9267.
+
+
+
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