diff --git a/mindscience/models/__init__.py b/mindscience/models/__init__.py
index 2cc514aa99bf2e676f033151dff32fb1a9228b98..880307d2dbcf753171c712202e15bdbb3e5759d9 100644
--- a/mindscience/models/__init__.py
+++ b/mindscience/models/__init__.py
@@ -19,13 +19,13 @@ This package contains various neural network models and related components.
 It includes implementations of popular architectures such as Vision Transformer,
 Graph Neural Networks, and other domain-specific models.
 """
-from .GraphCast import *
+from .graphcast import *
 from .layers import *
 from .neural_operator import *
 from .transformer import *
 
 __all__ = []
-__all__.extend(GraphCast.__all__)
+__all__.extend(graphcast.__all__)
 __all__.extend(layers.__all__)
 __all__.extend(neural_operator.__all__)
 __all__.extend(transformer.__all__)
diff --git a/mindscience/models/GraphCast/__init__.py b/mindscience/models/graphcast/__init__.py
similarity index 100%
rename from mindscience/models/GraphCast/__init__.py
rename to mindscience/models/graphcast/__init__.py
diff --git a/mindscience/models/GraphCast/graphcast.py b/mindscience/models/graphcast/graphcast.py
similarity index 96%
rename from mindscience/models/GraphCast/graphcast.py
rename to mindscience/models/graphcast/graphcast.py
index 56acace187627d9db3f5edeaedddbd9e8aac11ae..a4eb9113b4979df17f8350aac05946a21bd31fb2 100644
--- a/mindscience/models/GraphCast/graphcast.py
+++ b/mindscience/models/graphcast/graphcast.py
@@ -17,9 +17,7 @@ from __future__ import absolute_import
 
 import numpy as np
 
-import mindspore as ms
-import mindspore.nn as nn
-from mindspore import ops, set_seed, Tensor
+from mindspore import nn, ops, set_seed, Tensor
 
 set_seed(0)
 np.random.seed(0)
@@ -61,7 +59,7 @@ class MLPNet(nn.Cell):
                  out_channels,
                  latent_dims,
                  has_layernorm=True):
-        super(MLPNet, self).__init__()
+        super().__init__()
         cell_list = [nn.Dense(in_channels,
                               latent_dims,
                               has_bias=False,
@@ -96,7 +94,7 @@ class Embedder(nn.Cell):
                  eg2m_in_channels,
                  em2g_in_channels,
                  latent_dims):
-        super(Embedder, self).__init__()
+        super().__init__()
         self.v_g_embedder = MLPNet(in_channels=vg_in_channels, out_channels=latent_dims, latent_dims=latent_dims)
         self.v_m_embedder = MLPNet(in_channels=vm_in_channels, out_channels=latent_dims, latent_dims=latent_dims)
         self.e_m_embedder = MLPNet(in_channels=em_in_channels, out_channels=latent_dims, latent_dims=latent_dims)
@@ -132,7 +130,7 @@ class G2MGnn(nn.Cell):
                  latent_dims,
                  src_idx,
                  dst_idx):
-        super(G2MGnn, self).__init__()
+        super().__init__()
         self.interaction = InteractionLayer(node_in_channels,
                                             node_out_channels,
                                             edge_in_channels,
@@ -165,7 +163,7 @@ class Encoder(nn.Cell):
                  latent_dims,
                  src_idx,
                  dst_idx):
-        super(Encoder, self).__init__()
+        super().__init__()
         self.feature_embedder = Embedder(vg_in_channels,
                                          vm_in_channels,
                                          em_in_channels,
@@ -209,7 +207,7 @@ class InteractionLayer(nn.Cell):
                  src_idx,
                  dst_idx,
                  is_homo):
-        super(InteractionLayer, self).__init__()
+        super().__init__()
 
         # process node
         self.node_fn = MLPNet(in_channels=node_in_channels + edge_out_channels,
@@ -235,7 +233,7 @@ class InteractionLayer(nn.Cell):
         dst_feats = ops.gather(dst_node_feats, self.dst_idx, axis=0)
         updated_edge_feats = self.edge_fn(
             ops.Concat(-1)((src_feats, dst_feats, edge_feats)))
-        temp_node = ms.ops.Zeros()((dst_node_feats.shape[0], edge_feats.shape[-1]), updated_edge_feats.dtype)
+        temp_node = ops.Zeros()((dst_node_feats.shape[0], edge_feats.shape[-1]), updated_edge_feats.dtype)
         scattered_edge_feats = ops.TensorScatterAdd()(
             temp_node, self.dst_idx.reshape(-1, 1), updated_edge_feats)
         updated_dst_feats = self.node_fn(
@@ -256,7 +254,7 @@ class Processor(nn.Cell):
                  latent_dims,
                  src_idx,
                  dst_idx):
-        super(Processor, self).__init__()
+        super().__init__()
         self.processing_steps = processing_steps
         self.cell_list = nn.SequentialCell()
         for _ in range(self.processing_steps):
@@ -287,7 +285,7 @@ class M2GGnn(nn.Cell):
                  latent_dims,
                  src_idx,
                  dst_idx):
-        super(M2GGnn, self).__init__()
+        super().__init__()
         self.interaction = InteractionLayer(node_in_channels,
                                             node_out_channels,
                                             edge_in_channels,
@@ -320,7 +318,7 @@ class Decoder(nn.Cell):
                  latent_dims,
                  src_idx,
                  dst_idx):
-        super(Decoder, self).__init__()
+        super().__init__()
 
         self.m2g_gnn = M2GGnn(node_in_channels,
                               node_out_channels,
diff --git a/mindscience/models/GraphCast/graphcastnet.py b/mindscience/models/graphcast/graphcastnet.py
similarity index 98%
rename from mindscience/models/GraphCast/graphcastnet.py
rename to mindscience/models/graphcast/graphcastnet.py
index cc820a43b3df1fbcead0736bf890e1b6d632536f..49328faac70b65ffd75d097ec0bb0ef1d292209e 100644
--- a/mindscience/models/GraphCast/graphcastnet.py
+++ b/mindscience/models/graphcast/graphcastnet.py
@@ -14,7 +14,9 @@
 # ============================================================================
 """GraphCastNet base class"""
 
-from mindspore import nn, ops, Tensor
+from mindspore import nn
+from mindspore import ops
+from mindspore import Tensor
 from .graphcast import Encoder, Processor, Decoder
 
 
@@ -135,7 +137,7 @@ class GraphCastNet(nn.Cell):
                  per_variable_level_mean,
                  per_variable_level_std,
                  recompute=False):
-        super(GraphCastNet, self).__init__()
+        super().__init__()
         self.vg_out_channels = vg_out_channels
         self.mesh_node_feats = mesh_node_feats
         self.mesh_edge_feats = mesh_edge_feats