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ρ predict

About

This tool predicts the electronic density of molecules using a symmetry-adapted Gaussian process regression model (SA-GPR). Available trained models:

  • SA-GPR model trained on the sidechain-sidechain interaction subset of the biofragment database ( BFDB-SSI (H, C, N, O)[3][8]).
  • SA-GPR model trained on the sidechain-sidechain interaction subset of the biofragment database ( BFDB-SSI (H, C, N, O, S)[3][8]).

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 .

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).

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How to cite

Until the preprint is available from arXiv, please cithe this tool as:

K.R. Briling, O. Hernandez-Cuellar, J.W. Abbott, M. Ceriotti, C. Corminboeuf, (2024) "rho predict" https://github.com/lcmd-epfl/rho-prediction