Abstract
Deep learning has been shown to learn efficient representations for structured data such as images, text, or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accurately predicts chemical properties across compositional and configurational space on a variety of datasets. Beyond that, we analyze the obtained representations to find evidence that their spatial and chemical properties agree with chemical intuition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
K.T. Schütt, H. Glawe, F. Brockherde, A. Sanna, K.R. Müller, E. Gross, Phys. Rev. B 89(20), 205118 (2014)
H. Huo, M. Rupp, (2017, preprint). arXiv:1704.06439
F.A. Faber, A.S. Christensen, B. Huang, O.A. von Lilienfeld, J. Chem. Phys. 148(24), 241717 (2018)
S. De, A.P. Bartók, G. Csányi, M. Ceriotti, Phys. Chem. Chem. Phys. 18(20), 13754 (2016)
T. Morawietz, A. Singraber, C. Dellago, J. Behler, Proc. Natl. Acad. Sci. 113(30), 8368 (2016)
M. Gastegger, J. Behler, P. Marquetand, Chem. Sci. 8(10), 6924 (2017)
F.A. Faber, L. Hutchison, B. Huang, J. Gilmer, S.S. Schoenholz, G.E. Dahl, O. Vinyals, S. Kearnes, P.F. Riley, O.A. von Lilienfeld, J. Chem. Theory Comput. 13(11), 5255 (2017)
E.V. Podryabinkin, A.V. Shapeev, Comput. Mater. Sci. 140, 171 (2017)
F. Brockherde, L. Vogt, L. Li, M.E. Tuckerman, K. Burke, K.R. Müller, Nat. Commun. 8, 872 (2017)
A.P. Bartók, S. De, C. Poelking, N. Bernstein, J.R. Kermode, G. Csányi, M. Ceriotti, Sci. Adv. 3(12), e1701816 (2017)
K.T. Schütt, H.E. Sauceda, P.J. Kindermans, A. Tkatchenko, K.R. Müller, J. Chem. Phys. 148(24), 241722 (2018)
S. Chmiela, H.E. Sauceda, K.R. Müller, A. Tkatchenko, Towards exact molecular dynamics simulations with machine-learned force fields. Nat. Commun. 9(1), 1–10 (2018)
A. Ziletti, D. Kumar, M. Scheffler, L.M. Ghiringhelli, Nat. Commun. 9(1), 2775 (2018)
D. Dragoni, T.D. Daff, G. Csányi, N. Marzari, Phys. Rev. Mater. 2(1), 013808 (2018)
A.P. Bartók, M.C. Payne, R. Kondor, G. Csányi, Phys. Rev. Lett. 104(13), 136403 (2010)
M. Rupp, A. Tkatchenko, K.R. Müller, O.A. Von Lilienfeld, Phys. Rev. Lett. 108(5), 058301 (2012)
G. Montavon, K. Hansen, S. Fazli, M. Rupp, F. Biegler, A. Ziehe, A. Tkatchenko, A.V. Lilienfeld, K.R. Müller, in Advances in Neural Information Processing Systems 25, ed. by F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger (Curran Associates, Red Hook, 2012), pp. 440–448
K. Hansen, G. Montavon, F. Biegler, S. Fazli, M. Rupp, M. Scheffler, O.A. Von Lilienfeld, A. Tkatchenko, K.R. Müller, J. Chem. Theory Comput. 9(8), 3404 (2013)
K. Hansen, F. Biegler, R. Ramakrishnan, W. Pronobis, O.A. von Lilienfeld, K.R. Müller, A. Tkatchenko, J. Phys. Chem. Lett. 6, 2326 (2015)
S. Chmiela, A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt, K.R. Müller, Sci. Adv. 3(5), e1603015 (2017)
J. Behler, M. Parrinello, Phys. Rev. Lett. 98(14), 146401 (2007)
A.P. Bartók, R. Kondor, G. Csányi, Phys. Rev. B 87(18), 184115 (2013)
M. Gastegger, L. Schwiedrzik, M. Bittermann, F. Berzsenyi, P. Marquetand, J. Chem. Phys. 148(24), 241709 (2018)
W. Pronobis, K.T. Schütt, A. Tkatchenko, K.R. Müller, Eur. Phys. J. B 91(8), 178 (2018)
A.E. Sifain, N. Lubbers, B.T. Nebgen, J.S. Smith, A.Y. Lokhov, O. Isayev, A.E. Roitberg, K. Barros, S. Tretiak, J. Phys. Chem. Lett. 9(16), 4495 (2018)
K. Yao, J.E. Herr, D.W. Toth, R. Mckintyre, J. Parkhill, The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics. Chem. Sci. 9(8), 2261–2269 (2018)
K.T. Schütt, M. Gastegger, A. Tkatchenko, K.R. Müller, (2018, preprint). arXiv:1806.10349
K.T. Schütt, F. Arbabzadah, S. Chmiela, K.R. Müller, A. Tkatchenko, Nat. Commun. 8, 13890 (2017)
K.T. Schütt, P.J. Kindermans, H.E. Sauceda, S. Chmiela, A. Tkatchenko, K.R. Müller, Advances in Neural Information Processing Systems, vol. 30 (Curran Associates, Red Hook, 2017), pp. 992–1002
A. Pukrittayakamee, M. Malshe, M. Hagan, L. Raff, R. Narulkar, S. Bukkapatnum, R. Komanduri, J. Chem. Phys. 130(13), 134101 (2009)
M. Malshe, R. Narulkar, L.M. Raff, M. Hagan, S. Bukkapatnam, P.M. Agrawal, R. Komanduri, J. Chem. Phys. 130(18), 184102 (2009)
Y. LeCun, B. Boser, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Neural Comput. 1(4), 541 (1989)
X. Jia, B. De Brabandere, T. Tuytelaars, L.V. Gool, in Advances in Neural Information Processing Systems, ed. by D.D. Lee, M. Sugiyama, U.V. Luxburg, I. Guyon, R. Garnett, vol. 29 (Curran Associates, Red Hook, 2016), pp. 667–675
C. Dugas, Y. Bengio, F. Bélisle, C. Nadeau, R. Garcia, Advances in Neural Information Processing Systems (Curran Associates, Red Hook, 2001), pp. 472–478
D.A. Clevert, T. Unterthiner, S. Hochreiter, (2015, preprint). arXiv:1511.07289
F. Chollet, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2818–2826
J. Deng, W. Dong, R. Socher, L.J. Li, K. Li, L. Fei-Fei, IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (IEEE, Piscataway, 2009), pp. 248–255
D. Broomhead, D. Lowe, Complex Syst. 2, 321 (1988)
J. Moody, C.J. Darken, Neural Comput. 1(2), 281 (1989)
R. Ramakrishnan, P.O. Dral, M. Rupp, O.A. von Lilienfeld, Sci. Data 1, 140022 (2014)
I. Loshchilov, F. Hutter, (2016, preprint). arXiv:1608.03983
D.P. Kingma, J. Ba, (2014, preprint). arXiv:1412.6980
A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson, APL Mater. 1(1), 011002 (2013). https://doi.org/10.1063/1.4812323
Acknowledgements
The authors thank Michael Gastegger for valuable discussions and feedback. This work was supported by the Federal Ministry of Education and Research (BMBF) for the Berlin Big Data Center BBDC (01IS14013A) and the Berlin Center for Machine Learning (01IS18037A). Additional support was provided by the Institute for Information & Communications Technology Promotion and funded by the Korean government (MSIT) (No. 2017-0-00451, No. 2017-0-01779). A.T. acknowledges support from the European Research Council (ERC-CoG grant BeStMo).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Schütt, K.T., Tkatchenko, A., Müller, KR. (2020). Learning Representations of Molecules and Materials with Atomistic Neural Networks. In: Schütt, K., Chmiela, S., von Lilienfeld, O., Tkatchenko, A., Tsuda, K., Müller, KR. (eds) Machine Learning Meets Quantum Physics. Lecture Notes in Physics, vol 968. Springer, Cham. https://doi.org/10.1007/978-3-030-40245-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-40245-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-40244-0
Online ISBN: 978-3-030-40245-7
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)