Format fix. More options in readme

README.md

-# BOMLIP-CSP
-
-An open-source Python framework that integrates machine learning interatomic 
-potentials (MLIPs) with a tailored batched optimization strategy, enabling rapid, 
-unbiased structure prediction across the full density range
-
-## Perform the complete CSP process
-
-```sh
-git clone https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP.git --recursive && cd BOMLIP-CSP
-
-conda create -n BOMLIP_CSP python=3.10 -y && conda activate BOMLIP_CSP
-cd BOMLIP-CSP
-top_dir=$(pwd)
-cd $top_dir/mace-bench
-./reproduce/init_mace.sh && source util/env.sh
-sudo ./util/mps_start.sh
-
-cd $top_dir
-./csp.sh
-
-sudo ./util/mps_clean.sh
-```
-## Reproduce mace batch opt speedup.
-
-```sh
-#!/bin/bash
-
-git clone https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP.git --recursive && cd BOMLIP-CSP
-conda create -n BOMLIP_CSP python=3.10 -y && conda activate BOMLIP_CSP
-cd BOMLIP-CSP/mace-bench
-
-# initialize mace env.
-./reproduce/init_mace.sh && source util/env.sh
-sudo ./util/mps_start.sh
-cd reproduce
-
-# run baseline sub-test
-./subtest_baseline.sh
-
-# run baseline mixed test
-cd perf_v2_base
-./run_mace.sh
-
-# run BOMLIP_CSP sub-test
-cd ../
-./subtest.sh
-
-# run BOMLIP_CSP mixed test
-cd perf_v2_batch
-./opt.sh
-
-# clean mps
-./util/mps_clean.sh
-
-```
-
-## If you want to configure the 7net environment.
-
-```sh
-#!/bin/bash
-conda create -n 7net-cueq python=3.10 -y && conda activate 7net-cueq
-./reproduce/init_7net.sh && source util/env.sh
-
-# Use a fixed batch size for structural optimization
-python ../../scripts/mace_opt_batch.py --target_folder "../../data/perf_v2" \ 
-    --molecule_single 46 --gpu_offset 0 --n_gpus 4 --num_workers 4 \
-    --batch_size 2 --max_steps 3000 --filter1 UnitCellFilter \
-    --filter2 UnitCellFilter --optimizer1 BFGSFusedLS --optimizer2 BFGS \
-    --num_threads 2 --cueq true --use_ordered_files true --model sevennet
-```
-
-## License
-
-This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
-
-### Third-party Dependencies
-
-This project includes dependencies with various licenses:
-- **MACE**: MIT License (compatible)
-- **FairChem**: MIT License (compatible)
-- **SevenNet**: GPL v3 License (Note: GPL is a copyleft license)
-
-### License Compatibility Notice
-
-**Important**: This project can run completely without relying on SevenNet. 
-This project includes SevenNet as an optional dependency, which is licensed under GPL v3.
-If you use SevenNet functionality, you should be aware of the GPL licensing requirements.
-For commercial use or to avoid GPL restrictions, consider using only the MACE calculator 
-functionality.
-
-## Citation
-
-If you use this code in your research, please cite:
-
-```bibtex
-@software{BOMLIP_CSP,
-  author = {Chengxi Zhao, Zhaojia Ma, Dingrui Fan},
-  title = {BOMLIP_CSP: Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction},
-  year = {2025},
-  url = {https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP}
-}
+# BOMLIP-CSP
+
+An open-source Python framework that integrates machine learning interatomic 
+potentials (MLIPs) with a tailored batched optimization strategy, enabling rapid, 
+unbiased structure prediction across the full density range
+
+## Perform a complete CSP process
+
+```sh
+git clone https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP.git --recursive && cd BOMLIP-CSP
+
+conda create -n BOMLIP_CSP python=3.10 -y && conda activate BOMLIP_CSP
+cd BOMLIP-CSP/mace-bench
+./reproduce/init_mace.sh && source util/env.sh
+sudo ./util/mps_start.sh
+
+cd ..
+./csp.sh
+
+sudo ./util/mps_clean.sh
+```
+
+## Perform conformer search / structure generation / structure optimization separately
+
+In csp.sh, the argument --mode controls the jobs to do.
+Use conformer_only to perform conformer search task only.
+```sh
+python "${TOP_DIR}/main.py" --path ${TAR_DIR} --smiles "OC(=O)c1cc(O)c(O)c(O)c1.O" \
+    --molecule_num_in_cell 1,1 --space_group_list 13,14 --add_name KONTIQ --max_workers 16\
+     --num_generation 100 --generate_conformers 20 --use_conformers 4 --mode conformer_only > generate.log 2>&1
+```
+Or use structure_only to perform structure generation only.
+```sh
+python "${TOP_DIR}/main.py" --path ${TAR_DIR} --smiles "OC(=O)c1cc(O)c(O)c(O)c1.O" \
+    --molecule_num_in_cell 1,1 --space_group_list 13,14 --add_name KONTIQ --max_workers 16\
+     --num_generation 100 --generate_conformers 20 --use_conformers 4 --mode structure_only > generate.log 2>&1
+```
+Structure optimization is done by a seperate command
+```sh
+python "${TOP_DIR}/mace-bench/scripts/mace_opt_batch.py" ...
+```
+Change this command into a comment if you don't want to do that.
+
+## Reproduce mace batch opt speedup.
+
+```sh
+#!/bin/bash
+
+git clone https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP.git --recursive && cd BOMLIP-CSP
+conda create -n BOMLIP_CSP python=3.10 -y && conda activate BOMLIP_CSP
+cd BOMLIP-CSP/mace-bench
+
+# initialize mace env.
+./reproduce/init_mace.sh && source util/env.sh
+sudo ./util/mps_start.sh
+cd reproduce
+
+# run baseline sub-test
+./subtest_baseline.sh
+
+# run baseline mixed test
+cd perf_v2_base
+./run_mace.sh
+
+# run BOMLIP_CSP sub-test
+cd ../
+./subtest.sh
+
+# run BOMLIP_CSP mixed test
+cd perf_v2_batch
+./opt.sh
+
+# clean mps
+./util/mps_clean.sh
+
+```
+
+## If you want to configure the 7net environment.
+
+```sh
+#!/bin/bash
+conda create -n 7net-cueq python=3.10 -y && conda activate 7net-cueq
+./reproduce/init_7net.sh && source util/env.sh
+
+# Use a fixed batch size for structural optimization
+python ../../scripts/mace_opt_batch.py --target_folder "../../data/perf_v2" \ 
+    --molecule_single 46 --gpu_offset 0 --n_gpus 4 --num_workers 4 \
+    --batch_size 2 --max_steps 3000 --filter1 UnitCellFilter \
+    --filter2 UnitCellFilter --optimizer1 BFGSFusedLS --optimizer2 BFGS \
+    --num_threads 2 --cueq true --use_ordered_files true --model sevennet
+```
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+### Third-party Dependencies
+
+This project includes dependencies with various licenses:
+- **MACE**: MIT License (compatible)
+- **FairChem**: MIT License (compatible)
+- **SevenNet**: GPL v3 License (Note: GPL is a copyleft license)
+
+### License Compatibility Notice
+
+**Important**: This project can run completely without relying on SevenNet. 
+This project includes SevenNet as an optional dependency, which is licensed under GPL v3.
+If you use SevenNet functionality, you should be aware of the GPL licensing requirements.
+For commercial use or to avoid GPL restrictions, consider using only the MACE calculator 
+functionality.
+
+## Citation
+
+If you use this code in your research, please cite:
+
+```bibtex
+@software{BOMLIP_CSP,
+  author = {Chengxi Zhao, Zhaojia Ma, Dingrui Fan},
+  title = {BOMLIP_CSP: Integrating machine learning interatomic potentials with batched optimization for crystal structure prediction},
+  year = {2025},
+  url = {https://github.com/pic-ai-robotic-chemistry/BOMLIP-CSP}
+}
 ```
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