Abstract
Within the past few years, we have witnessed the rising of quantum machine learning (QML) models which infer electronic properties of molecules and materials, rather than solving approximations to the electronic Schrödinger equation. The increasing availability of large quantum mechanics reference datasets has enabled these developments. We review the basic theories and key ingredients of popular QML models such as choice of regressor, data of varying trustworthiness, the role of the representation, and the effect of training set selection. Throughout we emphasize the indispensable role of learning curves when it comes to the comparative assessment of different QML models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Axilrod BM, Teller E (1943) Interaction of the van der Waals type between three atoms. J Chem Phys 11(6):299–300. https://doi.org/10.1063/1.1723844, http://scitation.aip.org/content/aip/journal/jcp/11/6/10.1063/1.1723844
Bader RF (1990) Atoms in molecules: a quantum theory. Clarendon Press, Oxford
Bartók AP, Payne MC, Kondor R, Csányi G (2010) Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. Phys Rev Lett 104:136403. https://doi.org/10.1103/PhysRevLett.104.136403
Bartók AP, Kondor R, Csányi G (2013) On representing chemical environments. Phys Rev B 87:184115. https://doi.org/10.1103/PhysRevB.87.184115
Bartók AP, De S, Poelking C, Bernstein N, Kermode JR, Csányi G, Ceriotti M (2017) Machine learning unifies the modeling of materials and molecules. Sci Adv 3(12):e1701816 https://doi.org/10.1126/sciadv.1701816, http://advances.sciencemag.org/content/3/12/e1701816
Browning NJ, Ramakrishnan R, von Lilienfeld OA, Roethlisberger U (2017) Genetic optimization of training sets for improved machine learning models of molecular properties. J Phys Chem Lett 8(7):1351. https://doi.org/10.1021/acs.jpclett.7b00038
Csányi G, Albaret T, Payne MC, Vita AD (2004) “Learn on the fly”: a hybrid classical and quantum-mechanical molecular dynamics simulation. Phys Rev Lett 93:175503
De S, Bartok AP, Csanyi G, Ceriotti M (2016) Comparing molecules and solids across structural and alchemical space. Phys Chem Chem Phys 18:13754–13769. https://doi.org/10.1039/C6CP00415F
Faber FA, Christensen AS, Huang B, von Lilienfeld OA (2018) Alchemical and structural distribution based representation for improved QML. J Chem Phys 148:241717. https://doi.org/10.1063/1.5020710. arXiv preprint arXiv:171208417
Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, Riley PF, von Lilienfeld OA (2017) Machine learning prediction errors better than DFT accuracy. arXiv preprint arXiv:1702.05532. https://arxiv.org/abs/1702.05532
Fasshauer G, McCourt M (2016) Kernel-based approximation methods using MATLAB. World Scientific, Singapore
Fias S, Heidar-Zadeh F, Geerlings P, Ayers PW (2017) Chemical transferability of functional groups follows from the nearsightedness of electronic matter. Proc Natl Acad Sci 114(44):11633–11638
Ghiringhelli LM, Vybiral J, Levchenko SV, Draxl C, Scheffler M (2015) Big data of materials science: critical role of the descriptor. Phys Rev Lett 114:105503. https://doi.org/10.1103/PhysRevLett.114.105503
Greeley J, Jaramillo TF, Bonde J, Chorkendorff I, Nørskov JK (2006) Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nat Mater 5(11): 909–913
Hansen K, Biegler F, von Lilienfeld OA, Müller KR, Tkatchenko A (2015) Interaction potentials in molecules and non-local information in chemical space. J Phys Chem Lett 6:2326. https://doi.org/10.1021/acs.jpclett.5b00831
Huang B, von Lilienfeld OA (2016) Communication: understanding molecular representations in machine learning: the role of uniqueness and target similarity. J Chem Phys 145(16):161102. https://doi.org/10.1063/1.4964627
Huang B, von Lilienfeld OA (2017) Chemical space exploration with molecular genes and machine learning. arXiv preprint arXiv:170704146
Kennedy MC, O’Hagan A (2000) Predicting the output from a complex computer code when fast approximations are available. Biometrika 87(1):1–13
Kirkpatrick P, Ellis C (2004) Chemical space. Nature 432(7019):823–823. https://doi.org/10.1038/432823a
Kitaura K, Ikeo E, Asada T, Nakano T, Uebayasi M (1999) Fragment molecular orbital method: an approximate computational method for large molecules. Chem Phys Lett 313(3–4):701–706. https://doi.org/10.1016/S0009-2614(99)00874-X
Muto Y (1943) Force between nonpolar molecules. J Phys Math Soc Jpn 17:629–631
Pilania G, Gubernatis JE, Lookman T (2017) Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput Mater Sci 129:156–163
Podryabinkin EV, Shapeev AV (2017) Active learning of linearly parametrized interatomic potentials. Comput Mater Sci 140:171–180
Prodan E, Kohn W (2005) Nearsightedness of electronic matter. Proc Natl Acad Sci USA 102(33):11635–11638. https://doi.org/10.1073/pnas.0505436102
Ramakrishnan R, von Lilienfeld OA (2015) Many molecular properties from one kernel in chemical space. Chimia 69(4):182. https://doi.org/10.2533/chimia.2015.182, http://www.ingentaconnect.com/content/scs/chimia/2015/00000069/00000004/art00005
Ramakrishnan R, Dral P, Rupp M, von Lilienfeld OA (2014) Quantum chemistry structures and properties of 134 kilo molecules. Sci Data 1:140022. https://doi.org/10.1038/sdata.2014.22
Ramakrishnan R, Dral P, Rupp M, von Lilienfeld OA (2015a) Big data meets quantum chemistry approximations: the Δ-machine learning approach. J Chem Theory Comput 11:2087–2096. https://doi.org/10.1021/acs.jctc.5b00099
Ramakrishnan R, Hartmann M, Tapavicza E, von Lilienfeld OA (2015b) Electronic spectra from TDDFT and machine learning in chemical space. J Chem Phys 143:084111. http://arxiv.org/abs/1504.01966
Rappe AK, Casewit CJ, Colwell KS, Goddard WA III, Skiff WM (1992) UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc 114(25):10024–10035. https://doi.org/10.1021/ja00051a040
Rasmussen CE, Williams CKI (2006) Gaussian processes for machine learning. Adaptative computation and machine learning series. University Press Group Limited. https://books.google.ch/books?id=vWtwQgAACAAJ
Ruddigkeit L, van Deursen R, Blum LC, Reymond JL (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52(11): 2864–2875. https://doi.org/10.1021/ci300415d
Rupp M, Tkatchenko A, Müller KR, von Lilienfeld OA (2012) Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett 108(5):058301. https://doi.org/10.1103/PhysRevLett.108.058301
Samuel AL (2000) Some studies in machine learning using the game of checkers. IBM J Res Dev 44(1.2):206–226
Todeschini R, Consonni V (2008) Handbook of molecular descriptors, vol 11. Wiley, Weinheim
von Lilienfeld OA (2018) Quantum machine learning in chemical compound space. Angew Chemie Int Ed 57(16):4164–4169. https://doi.org/10.1002/anie.201709686
von Lilienfeld OA, Ramakrishnan R, Rupp M, Knoll A (2015) Fourier series of atomic radial distribution functions: a molecular fingerprint for machine learning models of quantum chemical properties. Int J Quantum Chem 115(16):1084–1093. https://doi.org/10.1002/qua.24912
Acknowledgments
We acknowledge support by the Swiss National Science foundation (No. PP00P2_138932, 407540_167186 NFP 75 Big Data, 200021_175747, NCCR MARVEL).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this entry
Cite this entry
Huang, B., Symonds, N.O., von Lilienfeld, O.A. (2020). Quantum Machine Learning in Chemistry and Materials. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-44677-6_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-44677-6_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44676-9
Online ISBN: 978-3-319-44677-6
eBook Packages: Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics