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1_hyperparameter_tuning_CGConvNet.py
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2_generate_job_submission_script_CGConvNet.py
2_generate_job_submission_script_CGConvNet.py
 
 
3_parse_hyperparameter_tuning_results.py
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3_parse_hyperparameter_tuning_results_CGConvNet.py
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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
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hyperparameter_tuning_result_CGConvNet.csv
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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
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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

crystal-graphforcefield-parameterizationgnn-model

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