Abstract
Geometry calculation of a molecule’s fundamental state is the starting point for the vast majority of molecular quantum chemistry researches. Few databases provide the results of these fundamental state calculations for large numbers of molecules. Our long term objective is to train machine learning models on such data to predict different kinds of molecules properties. Predicting the complete geometry would be a remarkable step forward. We first present results suggesting that it is difficult to train a neural network on this complex task. Then we demonstrate that a neural network can accurately predict the distance between atom pairs. The best results have been obtained by considering a neighborhood around each atom. This neighborhood depends on a cut-off distance and contains a limited number of atoms.
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Notes
- 1.
With the B3LYP functional (approximation on the Hamiltonian operator), the 6-31G(d) basis set (approximation on the monoelectronics functions), all in closed shell and gas phase.
- 2.
With the B3LYP functional, the 6-31G(2df,p) basis set, all in closed shell and gas phase.
References
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017). Machine learning unifies the modeling of materials and molecules. Science Advances, 3(12), e1701816.
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017). Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. Journal of Chemical Theory and Computation, 13(11), 5255–5264.
Gubaev, K., Podryabinkin, E. V., & Shapeev, A. V. (2018). Machine learning of molecular properties: Locality and active learning. The Journal of Chemical Physics, 148(24), 241727.
Hy, T. S., Trivedi, S., Pan, H., Anderson, B. M., & Kondor, R. (2018). Predicting molecular properties with covariant compositional networks. The Journal of Chemical Physics, 148(24), 241745.
Muller, K., Mika, S., Ratsch, G., Tsuda, K., & Scholkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks, 12(2), 181–201.
Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018). Machine learning for the structure-energy-property landscapes of molecular crystals. Chemical Science, 9(5), 1289–1300.
Nakata, M., & Shimazaki, T. (2017). PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry. Journal of Chemical Information and Modeling, 57(6), 1300–1308.
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014). Quantum chemistry structures and properties of 134 kilo molecules. Scientific Data, 1.
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012). Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17. Journal of Chemical Information and Modeling, 52(11), 2864–2875. PMID: 23088335.
Schneider, G. (2018). Generative Models for Artificially-intelligent Molecular Design. Molecular Informatics, 37(1–2), 1880131.
Scholkopf, B., & Smola, A. J. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Cambridge: MIT Press.
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017). Quantum-chemical insights from deep tensor neural networks. Nature Communications, 8, 13890.
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018). SchNet - A deep learning architecture for molecules and materials. The Journal of Chemical Physics, 148(24), 241722.
Sinitskiy, A. V., & Pande, V. S. (2018). Deep Neural Network Computes Electron Densities and Energies of a Large Set of Organic Molecules Faster than Density Functional Theory (DFT). arXiv:1809.02723 [physics].
Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C. J., Maruyama, B., Persson, K. A., & Aspuru-Guzik, A. (2018). Accelerating the discovery of materials for clean energy in the era of smart automation. Nature Reviews Materials, 3(5), 5–20.
Wang, Y., Xiao, J., Suzek, T. O., Zhang, J., Wang, J., & Bryant, S. H. (2009). PubChem: A public information system for analyzing bioactivities of small molecules. Nucleic Acids Research, 37, W623–W633.
Acknowledgements
This work was funded by a starting grant of the Research Commission of the University of Angers (QuChemPedIA). The calculation resources were provided by the LERIA laboratory. The authors would also like to thanks Jean-Mathieu Chantrein for his technical help.
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Leguy, J., Cauchy, T., Duval, B., Da Mota, B. (2022). Predicting Interatomic Distances of Molecular Quantum Chemistry Calculations. In: Jaziri, R., Martin, A., Rousset, MC., Boudjeloud-Assala, L., Guillet, F. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 1004. Springer, Cham. https://doi.org/10.1007/978-3-030-90287-2_8
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