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A deep learning Bayesian framework for attribute driven inverse materials design
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README.md

SLAMDUNCS

This repository contains codes for a deep learning inverse prediction framework "SLAMDUNCS: Structure Learning for Attribute-driven Materials Design Using Novel Conditional Sampling (SLAMDUNCS)" for efficient and accurate prediction of molecules exhibiting target properties. Databases used for training and testing the framework are also made available in this repository.

Details of the methodology and the results are presented in a publication: "A deep learning Bayesian framework for attribute driven inverse materials design".

Steps for code usage:

  1. Run bayes_pubchem.py
  2. Move files "Generated_smiles_" to the folder "Obtain_unique"
  3. Run postprocess_smiles.py
  4. Move "Generated_unique_smiles.smi" to the folder "Obtain_smiles"
  5. Run validate_structures.py
  6. Run get_final_structures.py
  7. File "Considered_smiles.smi" contains all the valid molecular structures predicted by SLAMDUNCS.
  8. For final screening, use forward prediction to ensure structures exhibit desired attributes. Use DFT to validate properties for selected structures.

Database used for training and testing the models is available in the files : a) Molecular_data_pubchem.jsn b) IQR_screened_redox_homolumo.jsn

Usage of the code is governed by "Samsung Publication License".

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