SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

which describes the physical laws underlying the interactions between nuclei and electrons. Here, Ĥel is the electronic Hamiltonian operator which describes effects due to the kinetic energy of the electrons, the interactions between electrons and nuclei, as well as the inter-electronic interactions. The wavefunction Ψel, an eigenfunction of Ĥel, captures the spatial distribution of electrons and the corresponding eigenvalue Eel represents the electronic energy of the system. Before the eigenvalue problem can be solved, a suitable functional expression for Ψel has to be found. A standard approach is to express the wavefunction as a Slater determinant Ψel = |ψ1 . . . ψn〉, an anti-symmetric product of molecular orbitals ψi, which are constructed as linear combinations of atom-centered basis functions ψi = ∑ j Cijφj . These atomic orbitals φj are typically taken to be products of radial functions Rl and spherical harmonics Y m l

[1]  Fabian B. Fuchs,et al.  SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks , 2020, NeurIPS.

[2]  A. Tkatchenko,et al.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems , 2021, Chemical reviews.

[3]  R. Bowen,et al.  Machine-learned approximations to Density Functional Theory Hamiltonians , 2016, Scientific Reports.

[4]  Frank Neese,et al.  The ORCA program system , 2012 .

[5]  Andrea Grisafi,et al.  Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems. , 2017, Physical review letters.

[6]  Klaus-Robert Müller,et al.  BIGDML—Towards accurate quantum machine learning force fields for materials , 2021, Nature Communications.

[7]  Klaus-Robert Müller,et al.  SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.

[8]  Jian Sun,et al.  Identity Mappings in Deep Residual Networks , 2016, ECCV.

[9]  Frank Neese,et al.  Software update: the ORCA program system, version 4.0 , 2018 .

[10]  Klaus-Robert Müller,et al.  Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules. , 2018, Journal of chemical theory and computation.

[11]  R. Kondor,et al.  Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.

[12]  D. Mattis Quantum Theory of Angular Momentum , 1981 .

[13]  Max Welling,et al.  Group Equivariant Convolutional Networks , 2016, ICML.

[14]  Lorenzo Rosasco,et al.  On Invariance and Selectivity in Representation Learning , 2015, ArXiv.

[15]  Joachim M. Buhmann,et al.  On Relevant Dimensions in Kernel Feature Spaces , 2008, J. Mach. Learn. Res..

[16]  Frederick R. Manby,et al.  OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features , 2020, The Journal of chemical physics.

[17]  Frank Noé,et al.  Deep-neural-network solution of the electronic Schrödinger equation , 2020, Nature Chemistry.

[18]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[19]  Michele Ceriotti,et al.  Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties. , 2021, The Journal of chemical physics.

[20]  Stéphane Mallat,et al.  Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties , 2018, The Journal of chemical physics.

[21]  Klaus-Robert Müller,et al.  Machine learning of accurate energy-conserving molecular force fields , 2016, Science Advances.

[22]  Gábor Csányi,et al.  Comparing molecules and solids across structural and alchemical space. , 2015, Physical chemistry chemical physics : PCCP.

[23]  Michele Parrinello,et al.  Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.

[24]  Klaus-Robert Müller,et al.  Machine Learning Force Fields , 2020, Chemical reviews.

[25]  M. Rupp,et al.  Machine learning of molecular electronic properties in chemical compound space , 2013, 1305.7074.

[26]  Alberto Fabrizio,et al.  Transferable Machine-Learning Model of the Electron Density , 2018, ACS central science.

[27]  Matthias Troyer,et al.  Solving the quantum many-body problem with artificial neural networks , 2016, Science.

[28]  Max Welling,et al.  Gauge Equivariant Convolutional Networks and the Icosahedral CNN 1 , 2019 .

[29]  Ryo Nagai,et al.  Completing density functional theory by machine learning hidden messages from molecules , 2019, npj Computational Materials.

[30]  Michael Gastegger,et al.  Machine learning of solvent effects on molecular spectra and reactions , 2020, Chemical Science.

[31]  Quoc V. Le,et al.  Searching for Activation Functions , 2018, arXiv.

[32]  Zhiwei Ding,et al.  Direct Prediction of Phonon Density of States With Euclidean Neural Networks , 2020, Advanced science.

[33]  Kevin Gimpel,et al.  Gaussian Error Linear Units (GELUs) , 2016 .

[34]  R. Hoffmann An Extended Hückel Theory. I. Hydrocarbons , 1963 .

[35]  Michael Gastegger,et al.  Equivariant message passing for the prediction of tensorial properties and molecular spectra , 2021, ICML.

[36]  J S Smith,et al.  ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.

[37]  Max Welling,et al.  Steerable CNNs , 2016, ICLR.

[38]  Maurice Weiler,et al.  A General Theory of Equivariant CNNs on Homogeneous Spaces , 2018, NeurIPS.

[39]  Klaus-Robert Müller,et al.  Finding Density Functionals with Machine Learning , 2011, Physical review letters.

[40]  Alexandre Tkatchenko,et al.  Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.

[41]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.

[42]  B. Reviews,et al.  Operator methods in quantum mechanics , 2007 .

[43]  Andrew Gordon Wilson,et al.  Generalizing Convolutional Neural Networks for Equivariance to Lie Groups on Arbitrary Continuous Data , 2020, ICML.

[44]  Anders S. Christensen,et al.  Operators in quantum machine learning: Response properties in chemical space. , 2018, The Journal of chemical physics.

[45]  Zhen Lin,et al.  Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network , 2018, NeurIPS.

[46]  Kristof T. Schütt,et al.  Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions , 2019, Nature Communications.

[47]  Risi Kondor,et al.  Cormorant: Covariant Molecular Neural Networks , 2019, NeurIPS.

[48]  Markus Meuwly,et al.  PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.

[49]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[50]  Risi Kondor,et al.  Covariant Compositional Networks For Learning Graphs , 2018, ICLR.

[51]  K. Müller,et al.  Towards exact molecular dynamics simulations with machine-learned force fields , 2018, Nature Communications.

[52]  Gabriel Peyré,et al.  Universal Invariant and Equivariant Graph Neural Networks , 2019, NeurIPS.

[53]  Stephan J. Garbin,et al.  Harmonic Networks: Deep Translation and Rotation Equivariance , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[54]  Nikos Komodakis,et al.  Rotation Equivariant Vector Field Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[55]  Kenji Doya,et al.  Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning , 2017, Neural Networks.

[56]  Andreas Ziehe,et al.  Learning Invariant Representations of Molecules for Atomization Energy Prediction , 2012, NIPS.

[57]  K-R Müller,et al.  SchNet - A deep learning architecture for molecules and materials. , 2017, The Journal of chemical physics.

[58]  Klaus-Robert Müller,et al.  SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects , 2021, Nature Communications.

[59]  Daniel G A Smith,et al.  Psi4 1.4: Open-source software for high-throughput quantum chemistry. , 2020, The Journal of chemical physics.

[60]  Li Li,et al.  Bypassing the Kohn-Sham equations with machine learning , 2016, Nature Communications.

[61]  Klaus-Robert Müller,et al.  Exploring chemical compound space with quantum-based machine learning , 2020, Nature Reviews Chemistry.

[62]  Li Li,et al.  Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds , 2018, ArXiv.

[63]  David A. Strubbe,et al.  Deep learning and density-functional theory , 2018, Physical Review A.

[64]  K. Müller,et al.  Quantum chemical accuracy from density functional approximations via machine learning , 2020, Nature Communications.

[65]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[66]  Stefan Goedecker,et al.  Linear scaling electronic structure methods in chemistry and physics , 2003, Comput. Sci. Eng..

[67]  Kristof T. Schütt,et al.  A deep neural network for molecular wave functions in quasi-atomic minimal basis representation. , 2020, The Journal of chemical physics.

[68]  Risi Kondor,et al.  Predicting molecular properties with covariant compositional networks. , 2018, The Journal of chemical physics.

[69]  Max Welling,et al.  3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data , 2018, NeurIPS.

[70]  Mordechai Kornbluth,et al.  SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials , 2021, ArXiv.