diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index f4307d2f..5ede3515 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -4,15 +4,11 @@ default_install_hook_types: [pre-commit, commit-msg]
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.1
+    rev: v0.1.3
     hooks:
       - id: ruff
         args: [--fix]
-
-  - repo: https://github.com/psf/black
-    rev: 23.10.0
-    hooks:
-      - id: black-jupyter
+      - id: ruff-format
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v4.5.0
@@ -31,6 +27,7 @@ repos:
     hooks:
       - id: codespell
         stages: [commit, commit-msg]
+        args: [--check-filenames]
 
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1
diff --git a/chgnet/data/dataset.py b/chgnet/data/dataset.py
index 087dd156..ee686fc0 100644
--- a/chgnet/data/dataset.py
+++ b/chgnet/data/dataset.py
@@ -125,7 +125,8 @@ def __getitem__(self, idx: int) -> tuple[CrystalGraph, dict]:
 
                 return crystal_graph, targets
 
-            # Omit structures with isolated atoms. Return another randomly selected structure
+            # Omit structures with isolated atoms. Return another randomly selected
+            # structure
             except Exception:
                 struct = self.structures[graph_id]
                 self.failed_graph_id[graph_id] = struct.composition.formula
@@ -491,7 +492,8 @@ def __init__(
 
         Args:
             data (str | dict): file path or dir name that contain all the JSONs
-            graph_converter (CrystalGraphConverter): Converts pymatgen.core.Structure to graph
+            graph_converter (CrystalGraphConverter): Converts pymatgen.core.Structure
+                to CrystalGraph object.
             targets ("ef" | "efs" | "efm" | "efsm"): The training targets.
                 Default = "efsm"
             energy_key (str, optional): the key of energy in the labels.
@@ -575,7 +577,8 @@ def __getitem__(self, idx):
                             targets["m"] = torch.abs(torch.tensor(mag, dtype=datatype))
                 return crystal_graph, targets
 
-            # Omit structures with isolated atoms. Return another randomly selected structure
+            # Omit structures with isolated atoms. Return another randomly selected
+            # structure
             except Exception:
                 structure = Structure.from_dict(self.data[mp_id][graph_id]["structure"])
                 self.failed_graph_id[graph_id] = structure.composition.formula
diff --git a/chgnet/graph/crystalgraph.py b/chgnet/graph/crystalgraph.py
index 67b85cdd..cd993e1e 100644
--- a/chgnet/graph/crystalgraph.py
+++ b/chgnet/graph/crystalgraph.py
@@ -30,8 +30,8 @@ def __init__(
     ) -> None:
         """Initialize the crystal graph.
 
-        Attention! This data class is not intended to be created manually. CrystalGraph should
-        be returned by a CrystalGraphConverter
+        Attention! This data class is not intended to be created manually. CrystalGraph
+        should be returned by a CrystalGraphConverter
 
         Args:
             atomic_number (Tensor): the atomic numbers of atoms in the structure
@@ -92,7 +92,8 @@ def __init__(
         self.composition = composition
         if len(directed2undirected) != 2 * len(undirected2directed):
             raise ValueError(
-                f"{graph_id} number of directed indices != 2 * number of undirected indices!"
+                f"{graph_id} number of directed indices ({len(directed2undirected)}) !="
+                f" 2 * number of undirected indices ({2 * len(undirected2directed)})!"
             )
 
     def to(self, device: str = "cpu") -> CrystalGraph:
diff --git a/chgnet/graph/graph.py b/chgnet/graph/graph.py
index 90c93508..72e6668b 100644
--- a/chgnet/graph/graph.py
+++ b/chgnet/graph/graph.py
@@ -89,8 +89,8 @@ def __eq__(self, other: object) -> bool:
             other (DirectedEdge): another DirectedEdge to compare to
 
         Returns:
-            bool: True if other is the same directed edge, or if other is the directed edge
-                with reverse direction of self, else False.
+            bool: True if other is the same directed edge, or if other is the directed
+                edge with reverse direction of self, else False.
         """
         self_img = (self.info or {}).get("image")
         other_img = (other.info or {}).get("image")
diff --git a/chgnet/model/basis.py b/chgnet/model/basis.py
index 1451c236..2468d2d5 100644
--- a/chgnet/model/basis.py
+++ b/chgnet/model/basis.py
@@ -172,8 +172,8 @@ def __init__(self, cutoff: float = 5, cutoff_coeff: float = 5) -> None:
             Default = 5
             cutoff_coeff (float): the strength of soft-Cutoff
             0 will disable the cutoff, returning 1 at every r
-            for positive numbers > 0, the smaller cutoff_coeff is, the faster this function
-                decays. Default = 5.
+            for positive numbers > 0, the smaller cutoff_coeff is, the faster this
+                function decays. Default = 5.
         """
         super().__init__()
         self.cutoff = cutoff
diff --git a/chgnet/model/composition_model.py b/chgnet/model/composition_model.py
index 01e1bb77..7f2c2825 100644
--- a/chgnet/model/composition_model.py
+++ b/chgnet/model/composition_model.py
@@ -46,7 +46,8 @@ def _get_energy(self, composition_feas: Tensor) -> Tensor:
         """Predict the energy given composition encoding.
 
         Args:
-            composition_feas: batched atom feature matrix [batch_size, total_num_elements].
+            composition_feas: batched atom feature matrix of shape
+                [batch_size, total_num_elements].
 
         Returns:
             prediction associated with each composition [batchsize].
@@ -111,7 +112,8 @@ def _get_energy(self, composition_feas: Tensor) -> Tensor:
         """Predict the energy given composition encoding.
 
         Args:
-            composition_feas: batched atom feature matrix [batch_size, total_num_elements].
+            composition_feas: batched atom feature matrix of shape
+                [batch_size, total_num_elements].
 
         Returns:
             prediction associated with each composition [batchsize].
diff --git a/chgnet/model/dynamics.py b/chgnet/model/dynamics.py
index 4d12f765..2348a050 100644
--- a/chgnet/model/dynamics.py
+++ b/chgnet/model/dynamics.py
@@ -424,8 +424,8 @@ def __init__(
                 Default = None
             loginterval (int): write to log file every interval steps
                 Default = 1
-            crystal_feas_logfile (str): open this file for recording crystal features during MD
-                Default = None
+            crystal_feas_logfile (str): open this file for recording crystal features
+                during MD. Default = None
             append_trajectory (bool): Whether to append to prev trajectory.
                 If false, previous trajectory gets overwritten
                 Default = False
@@ -541,8 +541,8 @@ def __init__(
                     bulk_modulus_au = eos.get_bulk_modulus(unit="eV/A^3")
                     compressibility_au = eos.get_compressibility(unit="A^3/eV")
                     print(
-                        f"Done bulk modulus calculation: "
-                        f"k = {round(bulk_modulus, 3)}GPa, {round(bulk_modulus_au, 3)}eV/A^3"
+                        f"Completed bulk modulus calculation: "
+                        f"k = {bulk_modulus:.3}GPa, {bulk_modulus_au:.3}eV/A^3"
                     )
                 except Exception:
                     bulk_modulus_au = 2 / 160.2176
@@ -667,8 +667,8 @@ def upper_triangular_cell(self, verbose: bool | None = False):
         while ASE's canonical description is lower-triangular cell.
 
         Args:
-            verbose (bool): Whether to notify user about upper-triangular cell transformation.
-                Default = False
+            verbose (bool): Whether to notify user about upper-triangular cell
+                transformation. Default = False
         """
         if not NPT._isuppertriangular(self.atoms.get_cell()):
             a, b, c, alpha, beta, gamma = self.atoms.cell.cellpar()
diff --git a/chgnet/model/encoders.py b/chgnet/model/encoders.py
index e8035718..d2eb4059 100644
--- a/chgnet/model/encoders.py
+++ b/chgnet/model/encoders.py
@@ -14,7 +14,8 @@ def __init__(self, atom_feature_dim: int, max_num_elements: int = 94) -> None:
 
         Args:
             atom_feature_dim (int): dimension of atomic embedding.
-            max_num_elements (int): maximum number of elements in the dataset. Default = 94
+            max_num_elements (int): maximum number of elements in the dataset.
+                Default = 94
         """
         super().__init__()
         self.embedding = nn.Embedding(max_num_elements, atom_feature_dim)
@@ -32,7 +33,9 @@ def forward(self, atomic_numbers: Tensor) -> Tensor:
 
 
 class BondEncoder(nn.Module):
-    """Encode a chemical bond given the position of two atoms using Gaussian Distance."""
+    """Encode a chemical bond given the positions of two atoms using Gaussian
+    distance.
+    """
 
     def __init__(
         self,
diff --git a/chgnet/model/model.py b/chgnet/model/model.py
index 521c1b7d..f593502c 100644
--- a/chgnet/model/model.py
+++ b/chgnet/model/model.py
@@ -534,8 +534,8 @@ def predict_structure(
         """Predict from pymatgen.core.Structure.
 
         Args:
-            structure (Structure | Sequence[Structure]): structure or a list of structures
-                to predict.
+            structure (Structure | Sequence[Structure]): structure or a list of
+                structures to predict.
             task (str): can be 'e' 'ef', 'em', 'efs', 'efsm'
                 Default = "efsm"
             return_site_energies (bool): whether to return per-site energies.
@@ -552,7 +552,8 @@ def predict_structure(
                 e (Tensor) : energy of structures float in eV/atom
                 f (Tensor) : force on atoms [num_atoms, 3] in eV/A
                 s (Tensor) : stress of structure [3, 3] in GPa
-                m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr magneton mu_B
+                m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr
+                    magneton mu_B
         """
         if self.graph_converter is None:
             raise ValueError("graph_converter cannot be None!")
@@ -598,7 +599,8 @@ def predict_graph(
                 e (Tensor) : energy of structures float in eV/atom
                 f (Tensor) : force on atoms [num_atoms, 3] in eV/A
                 s (Tensor) : stress of structure [3, 3] in GPa
-                m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr magneton mu_B
+                m (Tensor) : magnetic moments of sites [num_atoms, 3] in Bohr
+                    magneton mu_B
         """
         if not isinstance(graph, (CrystalGraph, Sequence)):
             raise ValueError(
diff --git a/chgnet/trainer/trainer.py b/chgnet/trainer/trainer.py
index 466baa0c..b1d9bc5a 100644
--- a/chgnet/trainer/trainer.py
+++ b/chgnet/trainer/trainer.py
@@ -63,8 +63,8 @@ def __init__(
                 Default = 0.1
             mag_loss_ratio (float): magmom loss ratio in loss function
                 Default = 0.1
-            optimizer (str): optimizer to update model. Can be "Adam", "SGD", "AdamW", "RAdam"
-                Default = 'Adam'
+            optimizer (str): optimizer to update model. Can be "Adam", "SGD", "AdamW",
+                "RAdam". Default = 'Adam'
             scheduler (str): learning rate scheduler. Can be "CosLR", "ExponentialLR",
                 "CosRestartLR". Default = 'CosLR'
             criterion (str): loss function criterion. Can be "MSE", "Huber", "MAE"
@@ -216,7 +216,8 @@ def train(
                 Default = None
             save_dir (str): the dir name to save the trained weights
                 Default = None
-            save_test_result (bool): whether to save the test set prediction in a json file
+            save_test_result (bool): Whether to save the test set prediction in a JSON
+                file. Default = False
             train_composition_model (bool): whether to train the composition model
                 (AtomRef), this is suggested when the fine-tuning dataset has large
                 elemental energy shift from the pretrained CHGNet, which typically comes
diff --git a/pyproject.toml b/pyproject.toml
index 8113797b..15aeb786 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,7 +15,7 @@ dependencies = [
     "cython>=0.29.26",
     "numpy>=1.21.6",
     "nvidia-ml-py3>=7.352.0",
-    "pymatgen",
+    "pymatgen>=2023.10.11",
     "torch>=1.11.0",
 ]
 classifiers = [
@@ -49,7 +49,6 @@ find = { include = ["chgnet*"], exclude = ["tests", "tests*"] }
 
 [tool.ruff]
 target-version = "py39"
-line-length = 95
 include = ["**/pyproject.toml", "*.ipynb", "*.py", "*.pyi"]
 select = [
     "B",    # flake8-bugbear
diff --git a/tests/test_crystal_graph.py b/tests/test_crystal_graph.py
index edddef76..d24d139a 100644
--- a/tests/test_crystal_graph.py
+++ b/tests/test_crystal_graph.py
@@ -345,9 +345,8 @@ def test_crystal_graph_stability_fast():
 def test_crystal_graph_repr():
     graph = converter_legacy(structure)
     assert (
-        repr(graph)
-        == "CrystalGraph(composition='Li2 Mn2 O4', atom_graph_cutoff=5, bond_graph_cutoff=3, "
-        "n_atoms=8, atom_graph_len=384, bond_graph_len=744)"
+        repr(graph) == "CrystalGraph(composition='Li2 Mn2 O4', atom_graph_cutoff=5, "
+        "bond_graph_cutoff=3, n_atoms=8, atom_graph_len=384, bond_graph_len=744)"
     )
 
 
diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 503ac7e4..736a6be8 100644
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -82,5 +82,6 @@ def test_structure_data_inconsistent_length():
 
     assert (
         str(exc.value)
-        == f"Inconsistent number of structures and labels: {len(structures)=}, {len(forces)=}"
+        == f"Inconsistent number of structures and labels: {len(structures)=}, "
+        f"{len(forces)=}"
     )