Analytic calculation and analysis of atomic polar tensors for molecules and materials using the Gaussian and plane waves approach

Machine learning the electric field response of condensed phase systems using perturbed neural network potentials

The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å-1 without loss in accuracy when compared to ab-initio molecular dynamics. This is remarkable because, in contrast to most previous approaches, the two neural networks on which PNNP MD is based are exclusively trained on molecular configurations sampled from zero-field MD simulations, demonstrating that the networks not only interpolate but also reliably extrapolate the field response. PNNP MD is based on rigorous theory yet it is simple, general, modular, and systematically improvable allowing us to obtain atomistic insight into the interaction of a wide range of condensed phase systems with external electric fields.

Boosting the Modeling of Infrared and Raman Spectra of Bulk Phase Chromophores with Machine Learning

In the field of vibrational spectroscopy simulation, hybrid approximations to Kohn-Sham density-functional theory (KS-DFT) are often considered computationally prohibitive due to the significant effort required to evaluate the exchange-correlation potential in planewave codes. In this Letter, we show that by leveraging the porting of KS-DFT on GPU and incorporating machine-learning techniques, simulating IR and Raman spectra of real-life chromophores in bulk aqueous solution becomes a routine application at this level of theory.

Vibrational circular dichroism spectra of natural products by means of the nuclear velocity perturbation theory

We present the application of the recently implemented nuclear velocity perturbation theory, using the combined Gaussian and plane waves approach in CP2K, to the vibrational circular dichroism (VCD) spectra of a set of natural products. Even though the calculations were carried out for isolated molecules in the gas-phase limit, neglecting inter-molecular interactions and anharmonic effects, the match between simulated and experimental spectra is reasonable. We also study the influence of different density functionals on the conformational search and the resulting VCD spectra via group coupling matrices (GCMs). The GCM analysis reveals that the VCD signal can in some cases arise from moieties which are close to each other and in other cases from moieties far from each other. Differences in spectra obtained using different exchange-correlation density functionals can be attributed to interaction terms between different moieties in the molecules changing their sign.

Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor

Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is, however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics has repeatedly proved to be suitable for this purpose; however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental and gives access to the IR spectrum, and more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion─a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.

Robust ΔSCF calculations with direct energy functional minimization methods and STEP for molecules and materials

The direct energy functional minimization method using the orbital transformation (OT) scheme in the program package CP2K has been employed for Δ self-consistent field (ΔSCF) calculations. The OT method for non-uniform molecular orbitals occupations allows us to apply the ΔSCF method for various kinds of molecules and periodic systems. Vertical excitation energies of heteroaromatic molecules and condensed phase systems, such as solvated ethylene and solvated uracil obeying periodic boundary conditions, are reported using the ΔSCF method. In addition, a Re-phosphate molecule attached to the surface of anatase (TiO₂) has been investigated. Additionally, we have implemented a recently proposed state-targeted energy projection ΔSCF algorithm [K. Carter-Fenk and J. M. Herbert, J. Chem. Theory Comput. 16(8), 5067-5082 (2020)] for diagonalization based SCF in CP2K. It is found that the OT scheme provides a smooth and robust SCF convergence for all investigated excitation energies and (non-)periodic systems.

Implementation of Nuclear Velocity Perturbation and Magnetic Field Perturbation Theory in CP2K and Their Application to Vibrational Circular Dichroism

We present the implementation of nuclear velocity perturbation theory (NVPT), using a pioneering combination of atom-centered (velocity-dependent) Gaussian basis functions and plane waves in the CP2K package. The atomic polar tensors (APTs) and atomic axial tensors (AATs) are evaluated in the velocity representation using efficient density functional perturbation theory. The presence of nonlocal pseudopotentials, the representation of potentials on numerical integration grids, and effects arising from the basis functions being centered on the atoms have been considered in the implementation. The Magnetic Field Perturbation Theory (MFPT) using gauge-including atomic orbitals is implemented in the same code and compared to the NVPT. Our implementation is the first to compare both approaches (MFPT and NVPT) in the same code. The implementation has been verified via sum rules and by investigating the gauge origin dependence of the AATs for a set of small molecules, oxirane, and fluoro-oxirane. We also present vibrational circular dichroism spectra that are related to the APTs and AATs, applying both theories.