From 737554e4863684f7f13285b85a4a1b0aa3aaaa86 Mon Sep 17 00:00:00 2001 From: Siyu Yang Date: Tue, 18 Mar 2025 07:32:38 +0000 Subject: [PATCH 1/2] rename docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md Signed-off-by: Siyu Yang --- .../{molecular_generation.md => structure_generation.md} | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) rename docs/mindchemistry/docs/source_zh_cn/user/{molecular_generation.md => structure_generation.md} (33%) diff --git a/docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md b/docs/mindchemistry/docs/source_zh_cn/user/structure_generation.md similarity index 33% rename from docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md rename to docs/mindchemistry/docs/source_zh_cn/user/structure_generation.md index 28a0e275c8..b8c9918776 100644 --- a/docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md +++ b/docs/mindchemistry/docs/source_zh_cn/user/structure_generation.md @@ -1,11 +1,11 @@ -# 分子生成 +# 结构生成 -[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md) +[![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindchemistry/docs/source_zh_cn/user/structure_generation.md) -分子生成，通过深度学习的生成模型去预测并生成粒子体系中的组成. 我们集成了基于主动学习进行高熵合金设计的方法，设计热膨胀系数极低的高熵合金组分。在主动学习流程中，首先基于AI模型生成候选的高熵合金组分，并基于预测模型和热动力学计算预测热膨胀系数对候选组分进行筛选，最终需要研究者基于实验验证确定最终的高熵合金组分。 +结构生成，通过深度学习的生成模型预测晶体材料的结构。我们集成了基于图神经网络和等变扩散模型的晶体生成模型 DiffCSP，它通过联合生成晶格和原子坐标来预测晶体结构，并利用周期性 E(3) 等变去噪模型来更好地模拟晶体的几何特性。它在计算成本上远低于传统的基于密度泛函理论的方法，且在晶体结构预测任务中表现出色。 ## 已支持网络 | 功能 | 模型 | 训练 | 推理 | 后端 | | :------------- | :-------------------- | :--- | :--- | :-------- | -| 分子生成| [high_entropy_alloy_design](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/high_entropy_alloy_design) | √ | √ | Ascend | +| 结构生成| [DiffCSP](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/diffcsp) | √ | √ | Ascend | -- Gitee From cf6e9e2a1a680672a3c5fdf2f3f206d3db0dd92c Mon Sep 17 00:00:00 2001 From: Siyu Yang Date: Tue, 18 Mar 2025 07:33:42 +0000 Subject: [PATCH 2/2] rename docs/mindchemistry/docs/source_en/user/molecular_generation.md Signed-off-by: Siyu Yang --- .../docs/source_en/user/molecular_generation.md | 11 ----------- .../docs/source_en/user/structure_generation.md | 11 +++++++++++ 2 files changed, 11 insertions(+), 11 deletions(-) delete mode 100644 docs/mindchemistry/docs/source_en/user/molecular_generation.md create mode 100644 docs/mindchemistry/docs/source_en/user/structure_generation.md diff --git a/docs/mindchemistry/docs/source_en/user/molecular_generation.md b/docs/mindchemistry/docs/source_en/user/molecular_generation.md deleted file mode 100644 index 69d8a7b22e..0000000000 --- a/docs/mindchemistry/docs/source_en/user/molecular_generation.md +++ /dev/null @@ -1,11 +0,0 @@ -# Molecular Generation - -[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindchemistry/docs/source_zh_cn/user/molecular_generation.md) - -Molecular generation, using deep learning generation models to predict and generate components in the particle system. We have integrated a method based on active learning for high entropy alloy design, designing high entropy alloy components with extremely low thermal expansion coefficients. In the active learning process, first, candidate high entropy alloy components are generated based on AI models, and the candidate components are screened based on predictive models and thermodynamic calculations to predict the thermal expansion coefficient. Finally, researchers need to determine the final high entropy alloy components based on experimental verification. - -## Supported Networks - -| Function | Model | Training | Inferring | Back-end | -|:---------------------| :-------------------- | :--- | :--- |:-----------| -| molecular generation | [high_entropy_alloy_design](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/high_entropy_alloy_design) | √ | √ | Ascend | diff --git a/docs/mindchemistry/docs/source_en/user/structure_generation.md b/docs/mindchemistry/docs/source_en/user/structure_generation.md new file mode 100644 index 0000000000..268d294c06 --- /dev/null +++ b/docs/mindchemistry/docs/source_en/user/structure_generation.md @@ -0,0 +1,11 @@ +# Structure Generation + +[![View Source On Gitee](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source_en.svg)](https://gitee.com/mindspore/docs/blob/master/docs/mindchemistry/docs/source_zh_cn/user/structure_generation.md) + +Structure generation, which is a structure generation model based on deep learning to predict the structures of crystalline materials. DiffCSP integrates graph neural networks and equivalent diffusion models to jointly generate crystal lattices and atomic coordinates. It also leverages a periodic E(3)-equivalent denouncing model to better simulate the geometric properties of crystals. Compared with traditional methods based on density functional theory, DiffCSP significantly reduces computational costs and demonstrates excellent performance in crystal structure prediction tasks. + +## Supported Networks + +| Function | Model | Training | Inferring | Back-end | +|:---------------------| :-------------------- | :--- | :--- |:-----------| +| structure generation | [DiffCSP](https://gitee.com/mindspore/mindscience/tree/master/MindChemistry/applications/diffcsp) | √ | √ | Ascend | -- Gitee