Accurate molecular polarizabilities with coupled-cluster theory and machine learning

Abstract

The molecular polarizability describes the tendency of a molecule to deform or polarize in response to an applied electric field. As such, this quantity governs key intra- and inter-molecular interactions such as induction and dispersion, plays a key role in determining the spectroscopic signatures of molecules, and is an essential ingredient in polarizable force fields and other empirical models for collective interactions. Compared to other ground-state properties, an accurate and reliable prediction of the molecular polarizability is considerably more difficult as this response quantity is quite sensitive to the description of the underlying molecular electronic structure. In this work, we present state-of-the-art quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear-response coupled-cluster singles and doubles theory (LR-CCSD). Using a symmetry-adapted machine-learning based approach, we demonstrate that it is possible to predict the molecular polarizability with LR-CCSD accuracy at a negligible computational cost. The employed model is quite robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (which includes challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid density functional theory (DFT). The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest.

References in corpus (5)

• Machine Learning Unifies the Modelling of Materials and Molecules
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• Automatic Selection of Atomic Fingerprints and Reference Configurations for Machine-Learning Potentials
• Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning
• Efficient nonparametric $n$-body force fields from machine learning

Cited by in corpus (56)

• Machine Learning Force Fields
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• Data-driven materials science: status, challenges and perspectives
• Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
• Physics-inspired structural representations for molecules and materials
• Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
• A Transferable Machine-Learning Model of the Electron Density
• Machine Learning in Nuclear Physics
• Incorporating long-range physics in atomic-scale machine learning
• On the Completeness of Atomic Structure Representations
• Atom-Density Representations for Machine Learning
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Deep neural network for the dielectric response of insulators
• Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
• Learning the electronic density of states in condensed matter
• Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles
• A Deep Dive into Machine Learning Density Functional Theory for Materials Science and Chemistry
• A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties
• Predicting electronic structures at any length scale with machine learning
• Equation of state of fluid methane from first principles with machine learning potentials
• Equivariant representations for molecular Hamiltonians and N-center atomic-scale properties
• Inexpensive modelling of quantum dynamics using path integral generalized Langevin equation thermostats
• Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor
• Predicting tensorial molecular properties with equivariant machine-learning models
• Molecular dynamics simulation with finite electric fields using Perturbed Neural Network Potentials
• Infrared spectra at coupled cluster accuracy from neural network representations
• A New Kind of Atlas of Zeolite Building Blocks
• NENCI-2021 Part I: A Large Benchmark Database of Non-Equilibrium Non-Covalent Interactions Emphasizing Close Intermolecular Contacts
• Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
• Modeling electronic response properties with an explicit-electron machine learning potential
• Compact atomic descriptors enable accurate predictions via linear models
• UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning
• Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies
• Fast and flexible long-range models for atomistic machine learning
• Uncertainty quantification and estimation in differential dynamic microscopy
• 3DReact: Geometric deep learning for chemical reactions
• Machine Learning Diffusion Monte Carlo Energies
• Predicting the Electronic Density Response of Condensed-Phase Systems to Electric Field Perturbations
• Mean-Field Density Matrix Decompositions
• Exploring the design space of machine-learning models for quantum chemistry with a fully differentiable framework
• SPA${}^{\mathrm{H}}$M(a,b): encoding the density information from guess Hamiltonian in quantum machine learning representations
• Machine learning at the atomic-scale
• Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space
• Graph-neural-network predictions of solid-state NMR parameters from spherical tensor decomposition
• Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning
• Machine learning the electronic structure of matter across temperatures
• A simple approach to rotationally invariant machine learning of avector quantity
• Physical machine learning outperforms "human learning" in Quantum Chemistry
• A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra
• Ab initio machine learning in chemical compound space
• Probing the Temporal Response of Liquid Water to a THz Pump Pulse Using Machine Learning-Accelerated Non-Equilibrium Molecular Dynamics
• General Learning of the Electric Response of Inorganic Materials
• Learning electron densities in the condensed phase
• Integer linear programming for unsupervised training set selection in molecular machine learning