refine docs (#1242)

HydrogenSulfate · web-flow · commit 5bccce4b50bf · 2025-11-03T16:59:04.000+08:00
* fix docs

* fix docs

* fix

* remove
trailing space
diff --git a/docs/zh/examples/MLP_LI.md b/docs/zh/examples/MLP_LI.md
@@ -34,12 +34,12 @@
 ## 模型训练命令
 === "模型训练命令"
 
-    ``` sh  
+    ``` sh
     # 训练模型
     python MLP_LI.py --train
 
     # 下载预训练模型（如果需要）
-    wget "https://paddle-org.bj.bcebos.com/paddlescience/models/MLP_LI/MLP_LI_pretrained.pdparams"
+    wget -nc "https://paddle-org.bj.bcebos.com/paddlescience/models/MLP_LI/MLP_LI_pretrained.pdparams"
 
 ## 完整代码
 
diff --git a/docs/zh/examples/RegAE.md b/docs/zh/examples/RegAE.md
@@ -354,5 +354,5 @@ paddle.incubate.optimizer.functional.minimize_lbfgs(objective_func, initial_posi
 | 支持硬件              | GPU、CPU     |
 | aistudio              | [notebook](https://aistudio.baidu.com/aistudio/projectdetail/5541961)     |
 
-请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.  
+请点击[此处](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576)查看本环境基本用法.
 Please click [here](https://ai.baidu.com/docs#/AIStudio_Project_Notebook/a38e5576) for more detailed instructions.
diff --git a/docs/zh/examples/catheter.md b/docs/zh/examples/catheter.md
diff --git a/docs/zh/examples/cfdgcn.md b/docs/zh/examples/cfdgcn.md
@@ -23,7 +23,7 @@
     # generalization experiments
     mpirun -np $((BATCH_SIZE+1)) python cfdgcn.py \
       TRAIN.batch_size=$((BATCH_SIZE)) \
-      TRAIN_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_train" \  
+      TRAIN_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_train" \
       TRAIN_MESH_GRAPH_PATH="./data/NACA0012_machsplit_noshock/mesh_fine. su2" \
       EVAL_DATA_DIR="./data/NACA0012_machsplit_noshock/outputs_test" \
       EVAL_MESH_GRAPH_PATH="./data/NACA0012_machsplit_noshock/mesh_fine.su2" \
diff --git a/docs/zh/examples/drivaernetplusplus.md b/docs/zh/examples/drivaernetplusplus.md
@@ -212,7 +212,7 @@ stl 源数据集下载教程参考，从[数据集下载地址](https://datavers
 
 2. 使用 wget 或 curl 直接下载 Globus Connect Personal：
 ``` sh
-wget https://downloads.globus.org/globus-connect-personal/linux/stable/globusconnectpersonal-latest.tgz
+wget -nc https://downloads.globus.org/globus-connect-personal/linux/stable/globusconnectpersonal-latest.tgz
 ```
 
 3. 从下载的 tarball 中提取文件。
diff --git a/docs/zh/examples/ifm.md b/docs/zh/examples/ifm.md
@@ -9,6 +9,8 @@
 === "模型训练命令"
 
     ``` sh
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/IFM/dataset.zip
+    unzip dataset.zip
     # 在tox21/sider/hiv/bace/bbbp等数据上训练模型MLP-IFM,embed_name可选IFM/None
     # mode/data_label/MODEL.embed_name 等参数可在conf/ifm.yaml进行配置
     python ifm.py data_label=tox21 MODEL.embed_name='IFM'
@@ -17,6 +19,8 @@
 === "模型评估命令"
 
     ``` sh
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/IFM/dataset.zip
+    unzip dataset.zip
     # 在tox21/sider/hiv/bace/bbbp等数据上评估模型MLP-IFM,embed_name可选IFM/None
     # 预训练模型的路径例如： pretrained/IFM/bace/model.pdparams 或使用自行训练的模型路径
     python ifm.py mode=eval data_label=tox21 MODEL.embed_name='IFM' EVAL.pretrained_model_path=pretrained/IFM/bace/model.pdparams
diff --git a/docs/zh/examples/latent_no.md b/docs/zh/examples/latent_no.md
@@ -80,10 +80,10 @@
 本节将讲解如何基于PaddleScience代码，实现对于隐空间神经算子模型的构建、训练、测试和评估。案例的目录结构如下。
 ``` log
 LatentNO/
-├── config  
+├── config
 │     ├── LatentNO-Darcy.yaml
 │     └── ...
-├── datas  
+├── datas
 │   ├── Darcy_train.npy
 │   ├── Darcy_val.npy
 │   └── ...
diff --git a/docs/zh/examples/nsfnet.md b/docs/zh/examples/nsfnet.md
@@ -78,7 +78,7 @@ $$v(x, y, z, t)= -a\left[e^{a y} \sin (a z+d x)+e^{a x} \cos (a y+d z)\right] e^
 
 $$w(x, y, z, t)= -a\left[e^{a z} \sin (a x+d y)+e^{a y} \cos (a z+d x)\right] e^{-d^2 t}, $$
 
-$$p(x, y, z, t)= -\frac{1}{2} a^2\left[e^{2 a x}+e^{2 a y}+e^{2 a z}+2 \sin (a x+d y) \cos (a z+d x) e^{a(y+z)} +2 \sin (a y+d z) \cos (a x+d y) e^{a(z+x)} +2 \sin (a z+d x) \cos (a y+d z) e^{a(x+y)}\right] e^{-2 d^2 t}.$$  
+$$p(x, y, z, t)= -\frac{1}{2} a^2\left[e^{2 a x}+e^{2 a y}+e^{2 a z}+2 \sin (a x+d y) \cos (a z+d x) e^{a(y+z)} +2 \sin (a y+d z) \cos (a x+d y) e^{a(z+x)} +2 \sin (a z+d x) \cos (a y+d z) e^{a(x+y)}\right] e^{-2 d^2 t}.$$
 ## 3. 问题求解
 ### 3.1 模型构建
 本文使用PINN经典的MLP模型进行训练。
diff --git a/docs/zh/examples/perovskite_solar_cells_nn.md b/docs/zh/examples/perovskite_solar_cells_nn.md
@@ -16,11 +16,15 @@
 
     ``` sh
     # 使用本地预训练模型
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/psc/data.zip
+    unzip data.zip
     python psc_nn.py mode=eval eval.pretrained_model_path="Your pdparams path"
     ```
 
     ``` sh
     # 或使用提供的预训练模型
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/psc/data.zip
+    unzip data.zip
     python psc_nn.py mode=eval eval.pretrained_model_path="https://paddle-org.bj.bcebos.com/paddlescience/models/PerovskiteSolarCells/solar_cell_pretrained.pdparams"
     ```
 
diff --git a/docs/zh/examples/smc_reac.md b/docs/zh/examples/smc_reac.md
@@ -12,7 +12,7 @@
     # linux
     wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx
     # windows
-    curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx -o data_set.xlsx
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx -o data_set.xlsx
     python smc_reac.py
     ```
 
@@ -22,7 +22,7 @@
     # linux
     wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx
     # windows
-    curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx -o data_set.xlsx
+    # curl https://paddle-org.bj.bcebos.com/paddlescience/datasets/SMCReac/data_set.xlsx -o data_set.xlsx
     python smc_reac.py mode=eval EVAL.pretrained_model_path=https://paddle-org.bj.bcebos.com/paddlescience/models/smc_reac/smc_reac_model.pdparams
     ```
 
@@ -43,7 +43,7 @@ $$
 ``` log
 smc_reac/
 ├──config/
-│   └── smc_reac.yaml  
+│   └── smc_reac.yaml
 ├── data_set.xlsx
 ├── requirements.txt
 └── smc_reac.py
diff --git a/docs/zh/examples/synthemol.md b/docs/zh/examples/synthemol.md
@@ -60,46 +60,46 @@ SyntheMol 是一种生成式模型，它在组合化学空间中进行探索，
 
 ### SyntheMol MCTS Algorithm
 
-**Requires:**  
+**Requires:**
 
-- Synthesis tree `T`  
-- Property prediction model `M`  
-- Maximum number of rollouts `n_rollout`  
-- Maximum number of reactions `n_reaction`  
+- Synthesis tree `T`
+- Property prediction model `M`
+- Maximum number of rollouts `n_rollout`
+- Maximum number of reactions `n_reaction`
 
 ---
 
-**function `MCTS()`:**  
-&nbsp;&nbsp;&nbsp;&nbsp;**for** `i = 1` to `n_rollout` **do**:  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`rollout(T.root)`  
-&nbsp;&nbsp;&nbsp;&nbsp;**end for**  
-&nbsp;&nbsp;&nbsp;&nbsp;**return** all visited nodes in `T` with:  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1 molecule and ≥ 1 reaction  
+**function `MCTS()`:**
+&nbsp;&nbsp;&nbsp;&nbsp;**for** `i = 1` to `n_rollout` **do**:
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;`rollout(T.root)`
+&nbsp;&nbsp;&nbsp;&nbsp;**end for**
+&nbsp;&nbsp;&nbsp;&nbsp;**return** all visited nodes in `T` with:
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;1 molecule and ≥ 1 reaction
 
 ---
 
-**function `rollout(N)`:**  
-&nbsp;&nbsp;&nbsp;&nbsp;**if** node `N` has undergone `≥ n_reaction` reactions **then**  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**return** property prediction score of `M` applied to molecules in `N`  
-&nbsp;&nbsp;&nbsp;&nbsp;**end if**  
-&nbsp;&nbsp;&nbsp;&nbsp;`E ← expand_node(N)`  
-&nbsp;&nbsp;&nbsp;&nbsp;`S ← select` child node in `E` with largest MCTS score  
-&nbsp;&nbsp;&nbsp;&nbsp;**return** `rollout(S)`  
+**function `rollout(N)`:**
+&nbsp;&nbsp;&nbsp;&nbsp;**if** node `N` has undergone `≥ n_reaction` reactions **then**
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**return** property prediction score of `M` applied to molecules in `N`
+&nbsp;&nbsp;&nbsp;&nbsp;**end if**
+&nbsp;&nbsp;&nbsp;&nbsp;`E ← expand_node(N)`
+&nbsp;&nbsp;&nbsp;&nbsp;`S ← select` child node in `E` with largest MCTS score
+&nbsp;&nbsp;&nbsp;&nbsp;**return** `rollout(S)`
 
 ---
 
-**function `expand_node(N)`:**  
-&nbsp;&nbsp;&nbsp;&nbsp;`E ← empty set of nodes`  
-&nbsp;&nbsp;&nbsp;&nbsp;**foreach** reaction `R` **do**  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**if** `R` is compatible with molecules in `N` **then**  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Add new node to `E` with each product of `R` applied to molecules in `N`  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end if**  
-&nbsp;&nbsp;&nbsp;&nbsp;**end for**  
-&nbsp;&nbsp;&nbsp;&nbsp;**foreach** building block `B` **do**  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**if** any reaction is compatible with `B` and molecules in `N` **then**  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Add new node to `E` with `B` and molecules in `N`  
-&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end if**  
-&nbsp;&nbsp;&nbsp;&nbsp;**end for**  
+**function `expand_node(N)`:**
+&nbsp;&nbsp;&nbsp;&nbsp;`E ← empty set of nodes`
+&nbsp;&nbsp;&nbsp;&nbsp;**foreach** reaction `R` **do**
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**if** `R` is compatible with molecules in `N` **then**
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Add new node to `E` with each product of `R` applied to molecules in `N`
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end if**
+&nbsp;&nbsp;&nbsp;&nbsp;**end for**
+&nbsp;&nbsp;&nbsp;&nbsp;**foreach** building block `B` **do**
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**if** any reaction is compatible with `B` and molecules in `N` **then**
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Add new node to `E` with `B` and molecules in `N`
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;**end if**
+&nbsp;&nbsp;&nbsp;&nbsp;**end for**
 &nbsp;&nbsp;&nbsp;&nbsp;**return** `E`
 
 ## 3. Synthemol模型实现
@@ -110,18 +110,18 @@ SyntheMol 是一种生成式模型，它在组合化学空间中进行探索，
 
 数据集采用了作者仓库 [Synthemol](https://github.com/swansonk14/SyntheMol) 的 Data.zip 数据集。
 
-训练集由 3 个化合物库组成：  
+训练集由 3 个化合物库组成：
 
-- 库 1 共 2371 个分子，来自 Pharmakon-1760 库（含 1 360 种 FDA 批准药物和 400 种国际批准药物）以及 800 种从植物、动物和微生物来源分离的天然产物。  
-- 库 2 为 Broad Drug Repurposing Hub，共 6 680 个分子，其中多数为 FDA 批准药物或临床候选化合物。  
+- 库 1 共 2371 个分子，来自 Pharmakon-1760 库（含 1 360 种 FDA 批准药物和 400 种国际批准药物）以及 800 种从植物、动物和微生物来源分离的天然产物。
+- 库 2 为 Broad Drug Repurposing Hub，共 6 680 个分子，其中多数为 FDA 批准药物或临床候选化合物。
 - 库 3 为一个小分子合成筛选库，含 5 376 个分子，系从 Broad Institute 更大的化合物库中随机抽样获得。
 
 所有 3 个库均以两次生物学重复的形式，对鲍曼不动杆菌 ATCC 17978 进行生长抑制活性筛选。实验流程如下：
 
-1. 将菌株于 37 °C 在 2 ml LB 培养基中过夜培养，随后以 1:10 000 稀释于新鲜 LB。  
-2. 取 49.5 µl（384 孔板）或 99 µl（96 孔板）菌液，使用手工或 Agilent Bravo 移液系统加入 Corning 平底微孔板。  
-3. 每孔加入待测化合物，终浓度 50 µM，终体积 50 µl（384 孔板）或 100 µl（96 孔板）。  
-4. 37 °C 静置孵育 16 h。  
+1. 将菌株于 37 °C 在 2 ml LB 培养基中过夜培养，随后以 1:10 000 稀释于新鲜 LB。
+2. 取 49.5 µl（384 孔板）或 99 µl（96 孔板）菌液，使用手工或 Agilent Bravo 移液系统加入 Corning 平底微孔板。
+3. 每孔加入待测化合物，终浓度 50 µM，终体积 50 µl（384 孔板）或 100 µl（96 孔板）。
+4. 37 °C 静置孵育 16 h。
 5. 使用 SpectraMax M3 酶标仪（Molecular Devices）于 600 nm 读取吸光度，数据按板内四分位均值归一化，随后进行汇总与阳性命中判定。
 
 更多详细信息，包括每个模型的超参数调整空间等，请参考作者原始论文。本仓库使用的具体超参数已在yaml配置文件中预设，可根据情况自行调节。
diff --git a/docs/zh/examples/tadf.md b/docs/zh/examples/tadf.md
@@ -14,7 +14,7 @@
 === "模型训练命令"
 
     ``` sh
-    # Est 预测:  
+    # Est 预测:
     cd TADF_Est
     wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/TADF/Est/Est.dat https://paddle-org.bj.bcebos.com/paddlescience/datasets/TADF/smis.txt
     python Est.py mode=train
@@ -64,15 +64,15 @@
 ``` log
 tadf/
 ├──TADF_angle/
-│   ├── config/  
-│   │    └── angle.yaml  
-│   ├── angle_model.py  
-│   ├── angle.dat  
-│   ├── angle.py  
-│   └── smis.txt  
+│   ├── config/
+│   │    └── angle.yaml
+│   ├── angle_model.py
+│   ├── angle.dat
+│   ├── angle.py
+│   └── smis.txt
 ├── TADF_Est/
-│   └── ...  
-├── TADF_f/  
+│   └── ...
+├── TADF_f/
 │   └── ...
 └── requirements.txt
 ```
diff --git a/docs/zh/examples/tgcn.md b/docs/zh/examples/tgcn.md
@@ -3,7 +3,7 @@
 === "模型训练命令"
 
     ``` sh
-    wget https://paddle-org.bj.bcebos.com/paddlescience/datasets/tgcn/tgcn_data.zip
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/tgcn/tgcn_data.zip
     unzip tgcn_data.zip
     python run.py data_name=PEMSD8
     # python run.py data_name=PEMSD4
@@ -12,11 +12,11 @@
 === "模型评估命令"
 
     ``` sh
-    wget https://paddle-org.bj.bcebos.com/paddlescience/datasets/tgcn/tgcn_data.zip
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/datasets/tgcn/tgcn_data.zip
     unzip tgcn_data.zip
-    wget https://paddle-org.bj.bcebos.com/paddlescience/models/tgcn/PEMSD8_pretrained_model.pdparams
+    wget -nc https://paddle-org.bj.bcebos.com/paddlescience/models/tgcn/PEMSD8_pretrained_model.pdparams
     python run.py data_name=PEMSD8 mode=eval EVAL.pretrained_model_path=PEMSD8_pretrained_model.pdparams
-    # wget https://paddle-org.bj.bcebos.com/paddlescience/models/tgcn/PEMSD4_pretrained_model.pdparams
+    # wget -nc https://paddle-org.bj.bcebos.com/paddlescience/models/tgcn/PEMSD4_pretrained_model.pdparams
     # python run.py data_name=PEMSD4 mode=eval EVAL.pretrained_model_path=PEMSD4_pretrained_model.pdparams
     ```
 
diff --git a/mkdocs.yml b/mkdocs.yml
@@ -25,7 +25,7 @@ repo_name: PaddleScience
 repo_url: "https://github.com/PaddlePaddle/PaddleScience"
 
 # Copyright
-copyright: Copyright &copy; 2022 - 2023 PaddlePaddle
+copyright: Copyright &copy; 2022 - 2025 PaddlePaddle
 
 # Page tree
 nav:
