Merge branch 'main' of github.com:Quantum-Accelerators/quacc

docs/install/codes.md

@@ -71,9 +71,9 @@ If you plan to use Q-Chem with Quacc, you will need to install `openbabel`. This
 conda install -c conda-forge openbabel
 ```
 
-## tblite
+## TBLite
 
-If you plan to use tblite with quacc, you will need to install the tblite interface with ASE support.
+If you plan to use TBLite with quacc, you will need to install the tblite interface with ASE support.
 
 ```bash
 pip install quacc[tblite] # only on Linux

docs/user/basics/wflow_overview.md

@@ -55,7 +55,7 @@ Everyone's computing needs are different, so we ensured that quacc is interopera
     - Extremely popular
     - Has significant support for running on HPC resources
     - It does not involve a centralized server or network connectivity
-    - Supports the pilot job model and advanced queuing schemes via Dask Jobqueue
+    - Supports adaptive scaling of compute resources
 
     Cons:
 

docs/user/wflow_engine/executors2.md

@@ -258,16 +258,16 @@ When deploying calculations for the first time, it's important to start simple,
 
 === "Dask"
 
-    From an interactive resource like a Jupyter Notebook or IPython kernel on the login node of the remote machine, run the following to instantiate a Dask `SLURMCluster`:
+    From an interactive resource like a Jupyter Notebook or IPython kernel on the login node of the remote machine, run the following to instantiate a Dask [`SLURMCluster`](https://jobqueue.dask.org/en/latest/generated/dask_jobqueue.SLURMCluster.html):
 
     ```python
     from dask.distributed import Client
     from dask_jobqueue import SLURMCluster
 
-    n_slurm_jobs = 1  # (1)!
-    n_nodes_per_calc = 1  # (2)!
-    n_cores_per_node = 48  # (3)!
-    mem_per_node = "64 GB"  # (4)!
+    n_slurm_jobs = 1
+    n_nodes_per_calc = 1
+    n_cores_per_node = 48
+    mem_per_node = "64 GB"
 
     cluster_kwargs = {
         # Dask worker options
@@ -276,7 +276,7 @@ When deploying calculations for the first time, it's important to start simple,
         "memory": mem_per_node,
         # SLURM options
         "shebang": "#!/bin/bash",
-        "account": "MyAccountName",  # (5)!
+        "account": "MyAccountName",  # (1)!
         "walltime": "00:10:00",
         "job_mem": "0",
         "job_script_prologue": [
@@ -292,15 +292,9 @@ When deploying calculations for the first time, it's important to start simple,
     client = Client(cluster)
     ```
 
-    1. Number of Slurm jobs to launch in parallel.
-
-    2. Number of nodes to reserve for each Slurm job.
-
-    3. Number of CPU cores per node.
-
-    4. Total memory per node.
+    1. Make sure to replace this with the account name to charge.
 
-    5. Make sure to replace this with the account name to charge.
+    Then run the following code:
 
     ```python
     from ase.build import bulk

pyproject.toml

@@ -53,7 +53,7 @@ phonons = ["phonopy>=2.20.0"]
 prefect = ["prefect>=2.13.1", "prefect-dask>=0.2.4", "dask-jobqueue>=0.8.2"]
 redun = ["redun>=0.16.2"]
 tblite = ["tblite[ase]>=0.3.0; platform_system=='Linux'"]
-dev = ["black>=23.7.0", "codecov-cli>=0.4.1", "docformatter>=1.7.5", "isort>=5.12.0", "pytest>=7.4.0", "pytest-cov>=3.0.0", "ruff>=0.0.285"]
+dev = ["black>=23.7.0", "codecov-cli>=0.4.1", "isort>=5.12.0", "pytest>=7.4.0", "pytest-cov>=3.0.0", "ruff>=0.0.285"]
 docs = [
     "blacken-docs>=1.16.0",
     "mkdocs-material>=9.1.21",