Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals

Abstract

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially when dealing with response properties of materials to e.g. electric fields, which require a good description of the perturbed electronic density. In this work, we use Gaussian process regression (GPR) to predict the static polarizability and dielectric susceptibility of molecules and molecular crystals. We combine this framework with ab initio molecular dynamics to predict their anharmonic vibrational Raman spectra. We stress the importance of data representation, symmetry, and locality, by comparing the performance of different flavors of GPR. In particular, we show the advantages of using a recently developed symmetry-adapted version of GPR. As an examplary application, we choose Paracetamol as an isolated molecule and in different crystal forms. We obtain accurate vibrational Raman spectra in all cases with less than 1000 training points, and obtain improvements when using a GPR trained on the molecular monomer as a baseline for the crystal GPR models. Finally, we show that our methodology is transferable across polymorphic forms: we can train the model on data for one structure, and still be able to accurately predict the spectrum for a second polymorph. This procedure provides an independent route to access electronic structure properties when performing force-evaluations on empirical force-fields or machine-learned potential energy surfaces.

References in corpus (20)

• Phonons and related properties of extended systems from density-functional perturbation theory
• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• On representing chemical environments
• Big Data of Materials Science - Critical Role of the Descriptor
• Long-range correlation energy calculated from coupled atomic response functions
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• Accurate molecular polarizabilities with coupled-cluster theory and machine learning
• Unsupervised machine learning in atomistic simulations, between predictions and understanding
• Atom-Density Representations for Machine Learning
• Fast and Accurate Uncertainty Estimation in Chemical Machine Learning
• Efficient nonparametric $n$-body force fields from machine learning
• Transferable atomic multipole machine learning models for small organic molecules
• Operators in Machine Learning: Response Properties in Chemical Space
• Structure and metallicity of phase V of hydrogen
• All-Electron, Real-Space Perturbation Theory for Homogeneous Electric Fields: Theory, Implementation, and Application within DFT
• Solvent Fluctuations and Nuclear Quantum Effects Modulate the Molecular Hyperpolarizability of Water
• Anharmonic Effects in the Low-Frequency Vibrational Modes of Aspirin and Paracetamol Crystals
• Constructing grids for molecular quantum dynamics using an autoencoder

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