Skip to content

Latest commit

 

History

History
122 lines (96 loc) · 8.24 KB

README.md

File metadata and controls

122 lines (96 loc) · 8.24 KB

ρ-predictor

A tool to predict the electronic density of molecules using a SA-GPR model

Requirements

Installation

You can install it by runing the comand:

pip install git+https://github.com/lcmd-epfl/rho-prediction.git

Before usage

Please be sure you have the weights and averages of a pre-trained model.

TODO: how to add the data

Usage

In a script

import rho_predictor
rho_predictor.predictor.predict_sagpr('path/to/mol.xyz', 'bfdb_HCNO')

or as a cli tool:

python -m  rho_predictor.predictor path/to/mol.xyz bfdb_HCNO

Acknowledgements

The authors acknowledge the National Centre of Competence in Research (NCCR) "Materials' Revolution: Computational Design and Discovery of Novel Materials (MARVEL)" of the Swiss National Science Foundation (SNSF, grant number 182892) and the European Research Council (ERC, grant agreement no 817977).

References

    Theory

  1. Briling, K. R.; Fabrizio, A.; Corminboeuf, C. Impact of Quantum-Chemical Metrics on the Machine Learning Prediction of Electron Density. J. Chem. Phys. 2021 , 155, 024107; doi: 10.1063/5.0055393 .
  2. Fabrizio, A.; Briling, K. R.; Girardier, D. D.; Corminboeuf, C. Learning On-Top: Regressing the On-Top Pair Density for Real-Space Visualization of Electron Correlation. J. Chem. Phys. 2020 , 153, 204111; doi: 10.1063/5.0033326 .
  3. Fabrizio, A.; Grisafi, A.; Meyer, B.; Ceriotti, M.; Corminboeuf, C. Electron Density Learning of Non-Covalent Systems. Chem. Sci. 2019, 10, 9424; doi: 10.1039/C9SC02696G .
  4. Grisafi, A.; Fabrizio, A.; Meyer, B.; Wilkins, D. M.; Corminboeuf, C.; Ceriotti, M. Transferable Machine-Learning Model of the Electron Density. ACS Cent. Sci. 2019 , 5, 57; doi: 10.1021/acscentsci.8b00551 .

    5. Representations

  5. Grisafi, A.; Wilkins, D. M.; Csányi, G.; Ceriotti, M. Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. Phys. Rev. Lett. 2018, 120, 036002; doi: 10.1103/physrevlett.120.036002 .
  6. Bartók, A. P.; Kondor, R.; Csányi, G. On Representing Chemical Environments. Phys. Rev. B 2013, 87; doi: 10.1103/physrevb.87.184115 .

    7. Examples of applications

  7. Vela, S.; Fabrizio, A.; Briling, K. R.; Corminboeuf, C. Learning the Exciton Properties of Azo-Dyes. J. Phys. Chem. Lett. 2021 , 12, 5957; doi: 10.1021/acs.jpclett.1c01425 .
  8. Fabrizio, A.; Briling, K.; Grisafi, A.; Corminboeuf, C. Learning (From) the Electron Density: Transferability, Conformational and Chemical Diversity. Chimia 2020, 74, 232; doi: 10.2533/chimia.2020.232 .