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final_evaluation_predictions
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job_submission_script_CGConvNet
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pretrained_model_CGConvNet
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train_dataset/processed
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validation_dataset
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0_preprocessing.py
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1_hyperparameter_tuning_CGConvNet.py
1_hyperparameter_tuning_CGConvNet.py
 
 
2_generate_job_submission_script_CGConvNet.py
2_generate_job_submission_script_CGConvNet.py
 
 
3_parse_hyperparameter_tuning_results.py
3_parse_hyperparameter_tuning_results.py
 
 
3_parse_hyperparameter_tuning_results_CGConvNet.py
3_parse_hyperparameter_tuning_results_CGConvNet.py
 
 
4_make_prediction.py
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5_results_analysis.py
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README.md
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data.json
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ffbonded.itp
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ffnonbonded.itp
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final_evaluation_modified.py
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helper.py
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hyperparameter_tuning_result.csv
hyperparameter_tuning_result.csv
 
 
hyperparameter_tuning_result_CGConvNet.csv
hyperparameter_tuning_result_CGConvNet.csv
 
 
make_permutation.py
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opls.par
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permutation_example.json
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permutation_example_masked.json
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plot_hp_tuning_results.py
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plot_hp_tuning_results_CGConvNet.py
plot_hp_tuning_results_CGConvNet.py
 
 
plot_val_loss.py
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requirements.txt
requirements.txt
 
 
validation_example.json
validation_example.json
 
 

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Molecular Nexus

Accurate force-field parameterization using graph neural network

CGConvNet that enables accurate parameterization of OPLS (Optimized Potentials for Liquid Simulations) force field for molecules.

Authors

Ray Zhu

Kha Trinh

Oliver Tang

Jaehee Park

License

Molecular Nexus is released under the MIT License

About

Graph neural network for OPLS force field parameterization

Topics

forcefield-parameterization gnn-model crystal-graph

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  • Python 54.5%
  • Shell 42.5%
  • Jupyter Notebook 3.0%