diff --git a/pybamm/solvers/jax_bdf_solver.py b/pybamm/solvers/jax_bdf_solver.py
index 59c5459d12..d9024117b9 100644
--- a/pybamm/solvers/jax_bdf_solver.py
+++ b/pybamm/solvers/jax_bdf_solver.py
@@ -166,7 +166,7 @@ def _bdf_init(fun, jac, mass, t0, y0, h0, rtol, atol):
     state['rtol'] = rtol
     state['M'] = mass
     EPS = jnp.finfo(y0.dtype).eps
-    state['newton_tol'] = jnp.max((10 * EPS / rtol, jnp.min((0.03, rtol ** 0.5))))
+    state['newton_tol'] = jnp.maximum(10 * EPS / rtol, jnp.minimum(0.03, rtol ** 0.5))
 
     scale_y0 = atol + rtol * jnp.abs(y0)
     y0, not_converged = _select_initial_conditions(
@@ -325,7 +325,7 @@ def _select_initial_step(atol, rtol, fun, t0, y0, f0, h0):
     d2 = jnp.sqrt(jnp.mean(((f1 - f0) / scale)**2))
     order = 1
     h1 = h0 * d2 ** (-1 / (order + 1))
-    return jnp.min((100 * h0, h1))
+    return jnp.minimum(100 * h0, h1)
 
 
 def _predict(state, D):
@@ -559,7 +559,7 @@ def _prepare_next_step_order_change(state, d, y, n_iter):
     max_index = jnp.argmax(factors)
     order += max_index - 1
 
-    factor = jnp.min((MAX_FACTOR, safety * factors[max_index]))
+    factor = jnp.minimum(MAX_FACTOR, safety * factors[max_index])
 
     new_state = _update_step_size_and_lu(state._replace(D=D, order=order), factor)
     return new_state
@@ -599,9 +599,9 @@ def while_body(while_state):
             state
         )
 
-        #if not_converged * updated_jacobian:
+        # if not_converged * updated_jacobian:
         #    print('not converged, update step size by 0.3')
-        #if not_converged * (updated_jacobian == False):
+        # if not_converged * (updated_jacobian == False):
         #    print('not converged, update jacobian')
 
         # if not converged and jacobian not updated, then update the jacobian and try
@@ -626,11 +626,11 @@ def while_body(while_state):
         error_norm = rms_norm(error / scale_y)
 
         # calculate optimal step size factor as per eq 2.46 of [2]
-        factor = jnp.max((MIN_FACTOR,
-                          safety *
-                          error_norm ** (-1 / (state.order + 1))))
+        factor = jnp.maximum(MIN_FACTOR,
+                             safety *
+                             error_norm ** (-1 / (state.order + 1)))
 
-        #if converged * (error_norm > 1):
+        # if converged * (error_norm > 1):
         #    print('converged, but error is too large',error_norm, factor, d, scale_y)
 
         (state, step_accepted) = tree_multimap(