add README.md, 5-updated-bt.py, myrun-bt-updated to introduce --mlp

README.md

@@ -25,12 +25,6 @@ conda activate htocsp
 mamba env update --file environment.yml
 ```
 
-## MACE Setup
-To add
-
-## UMA Setup
-To add
-
 ## CHARMM Setup
 One can request a [free academic version of CHARMM](https://brooks.chem.lsa.umich.edu/register/) and then install it via the following commands.
 *Note, make sure you compile charmm with the simplest option with qchem, openmm, quantum and colfft.*
@@ -77,6 +71,27 @@ $ charmm < charmm.in
 
 You should see quickly see the output of `NORMAL TERMINATION`. 
 
+## MACE Setup
+
+MACE setup does not require any additional installation beyond what is already included in the main `htocsp` environment. No extra configuration is needed—simply ensure you have completed the **Python Setup** and **CHARMM Setup** sections above.
+
+## UMA Setup
+
+Create the UMA environment from the `environment_UMA.yml` file.
+
+### HuggingFace Token Login
+
+This UMA workflow requires access to models hosted on [HuggingFace](https://huggingface.co), you'll need to authenticate with a HuggingFace token. Generate or retrieve your token from [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens) and then log in:
+
+```bash
+conda activate htocsp-uma
+python - << 'EOF'
+from huggingface_hub import login
+login(token="YOUR_TOKEN_HERE")
+EOF
+```
+
+Replace `YOUR_TOKEN_HERE` with your actual HuggingFace API token. This will store your credentials locally for future use.
 
 ## Quick Test 
 

examples/5-updated-bt.py

@@ -0,0 +1,48 @@
+"""
+This is an example to perform CSP based on the reference crystal.
+The structures with good matches will be output to *-matched.cif.
+"""
+from pyxtal.optimize import WFS, DFS, QRS
+from pyxtal import pyxtal
+import argparse
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-g", "--gen", dest="gen", type=int, default=1,
+                        help="Number of generation, default: 5")
+    parser.add_argument("-p", "--pop", dest="pop", type=int, default=10,
+                        help="Population size, default: 10")
+    parser.add_argument("-n", "--ncpu", dest="ncpu", type=int, default=1,
+                        help="cpu number, default: 1")
+    parser.add_argument("-a", "--algo", dest="algo", default='WFS',
+                        help="algorithm, default: WFS")
+    parser.add_argument("--ffstyle", dest='ffstyle', default='gaff',
+                        help="forcefield style, default: gaff")
+    parser.add_argument("--mlp", dest="mlp", default="MACE",
+                        help="Choose MLP calculator: MACE, UMA, NequIP, etc.")
+    parser.add_argument("--skip_mlp", dest="skip_mlp", action="store_true",
+                        help="Disable MLP optimization stage")
+    parser.add_argument("--check", dest='stable', action='store_true',
+                        help="enable stability check")
+    parser.add_argument("-c", "--code", dest="code",
+                        help="CSD code")
+
+    options = parser.parse_args()
+    data = {"Sp-HOF-5a_UMA": "Nc8nc(N)nc(c7ccc(/C(=C(c2ccc(c1nc(N)nc(N)n1)cc2)/c4ccc(c3nc(N)nc(N)n3)cc4)c6ccc(c5nc(N)nc(N)n5)cc6)cc7)n8"}
+    sg = [1,7,12]
+    smiles = data[options.code]
+    # Sampling
+    fun = globals().get(options.algo)
+    go = fun(smiles,
+             options.code,
+             sg,
+             #fracs = [0.8, 0.2],
+             tag = options.code.lower(),
+             N_gen = options.gen,
+             N_pop = options.pop,
+             N_cpu = options.ncpu,
+             ff_style = options.ffstyle,
+             skip_mlp = False, #not options.skip_mlp,
+             mlp = options.mlp,
+             check_stable = options.stable)
+    go.run()
+

examples/myrun-bt-updated

@@ -0,0 +1,26 @@
+#!/bin/sh -l
+#SBATCH --partition=Orion
+#SBATCH -J Sp-HOF-5a_UMA
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=96G     
+#SBATCH --time=120:00:00
+#SBATCH --hint=nomultithread
+
+# stop hidden threading in each worker (NumPy/MKL/OpenBLAS/etc.)
+export OMP_NUM_THREADS=1
+export MKL_NUM_THREADS=1
+export OPENBLAS_NUM_THREADS=1
+export NUMEXPR_NUM_THREADS=1
+export BLIS_NUM_THREADS=1
+export VECLIB_MAXIMUM_THREADS=1
+export TBB_NUM_THREADS=1
+export MKL_DYNAMIC=FALSE
+export OPENBLAS_MAIN_FREE=1
+
+# Print the hostname of the node executing this job
+echo "Running on node: $(hostname)"
+NCPU=$SLURM_CPUS_PER_TASK
+srun python 5-updated-bt.py -a DFS -p 32 --mlp "UMA" -n ${NCPU} -g 2500 -c ${SLURM_JOB_NAME} > log-${SLURM_JOB_NAME}
+