Some opinions on MD-based vibrational spectroscopy of gas phase molecules and their assembly: An overview of what has been achieved and where to go

Selective reactivity of glycosyl cation stereoisomers: the role of intramolecular hydrogen bonding

The impact of the stereochemistry of the glycosyl cation species upon its dynamic properties is examined together with their vibrational spectra in order to gain insight into the effects of configurational isomerism on conformer dynamics and proton mobility. Ab initio molecular dynamics (AIMD) simulations and infrared multiple photon dissociation (IRMPD) spectroscopy explore the conformational and reactive dynamics of two pairs of glycosyl cation isomers: (1) protonated α- and β- anomers of methyl-D-galactopyranoside and (2) the oxocarbenium ions of the D-aldohexose C2 epimers galactose and talose. Analysis of these simulations together with experimental spectroscopy, interpreted by anharmonic calculations, points to the key role played by the intramolecular hydrogen bonds which are present in a unique pattern and extent in each isomer. We find that the reactivity of galactoside stereoisomers toward acid-catalyzed nucleophilic substitution, as gauged by the ability to form free oxocarbenium ions, differs markedly in a way that agrees with experimental measurements in the condensed phase. Other properties such as conformer stability and vibrational transitions were also found to reflect the characteristic hydrogen bonding interactions present in each isomer. In both systems, the stereochemistry is shown to determine the strength of intramolecular hydrogen bonding as well as between which substituents proton transfer is possible. We expect that the critical impact of non-covalent interactions on stereoisomer selectivity may be a widely found phenomenon whose effects should be further investigated.

Probing the binding and activation of small molecules by gas-phase transition metal clusters via IR spectroscopy

Isolated transition metal clusters have been established as useful models for extended metal surfaces or deposited metal particles, to improve the understanding of their surface chemistry and of catalytic reactions. For this objective, an important milestone has been the development of experimental methods for the size-specific structural characterization of clusters and cluster complexes in the gas phase. This review focusses on the characterization of molecular ligands, their binding and activation by small transition metal clusters, using cluster-size specific infrared action spectroscopy. A comprehensive overview and a critical discussion of the experimental data available to date is provided, reaching from the initial results obtained using line-tuneable CO₂ lasers to present-day studies applying infrared free electron lasers as well as other intense and broadly tuneable IR laser sources.

Algorithmic Graph Theory, Reinforcement Learning and Game Theory in MD Simulations: From 3D Structures to Topological 2D-Molecular Graphs (2D-MolGraphs) and Vice Versa

This paper reviews graph-theory-based methods that were recently developed in our group for post-processing molecular dynamics trajectories. We show that the use of algorithmic graph theory not only provides a direct and fast methodology to identify conformers sampled over time but also allows to follow the interconversions between the conformers through graphs of transitions in time. Examples of gas phase molecules and inhomogeneous aqueous solid interfaces are presented to demonstrate the power of topological 2D graphs and their versatility for post-processing molecular dynamics trajectories. An even more complex challenge is to predict 3D structures from topological 2D graphs. Our first attempts to tackle such a challenge are presented with the development of game theory and reinforcement learning methods for predicting the 3D structure of a gas-phase peptide.

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.

Unsupervised Machine Learning Neural Gas Algorithm for Accurate Evaluations of the Hessian Matrix in Molecular Dynamics

The Hessian matrix of the potential energy of molecular systems is employed not only in geometry optimizations or high-order molecular dynamics integrators but also in many other molecular procedures, such as instantaneous normal mode analysis, force field construction, instanton calculations, and semiclassical initial value representation molecular dynamics, to name a few. Here, we present an algorithm for the calculation of the approximated Hessian in molecular dynamics. The algorithm belongs to the family of unsupervised machine learning methods, and it is based on the neural gas idea, where neurons are molecular configurations whose Hessians are adopted for groups of molecular dynamics configurations with similar geometries. The method is tested on several molecular systems of different dimensionalities both in terms of accuracy and computational time versus calculating the Hessian matrix at each time-step, that is, without any approximation, and other Hessian approximation schemes. Finally, the method is applied to the on-the-fly, full-dimensional simulation of a small synthetic peptide (the 46 atom N-acetyl-l-phenylalaninyl-l-methionine amide) at the level of DFT-B3LYP-D/6-31G* theory, from which the semiclassical vibrational power spectrum is calculated.

Proton in the ring: spectroscopy and dynamics of proton bonding in macrocycle cavities

The proton bond is a paradigmatic quantum molecular interaction and a major driving force of supramolecular chemistry. The ring cavities of crown ethers provide an intriguing environment, promoting competitive proton sharing with multiple coordination anchors. This study shows that protons confined in crown ether cavities form dynamic bonds that migrate to varying pairs of coordinating atoms when allowed by the flexibility of the macrocycle backbone. Prototypic native crown ethers (12-crown-4, 15-crown-5 and 18-crown-6) and aza-crown ethers (cyclen, 1-aza-18-crown-6 and hexacyclen) are investigated. For each system, Infrared action spectroscopy experiments and ab initio Molecular Dynamics computations are employed to elucidate the structural effects associated with proton diffusion and its entanglement with the conformational and vibrational dynamics of the protonated host.

Comparative studies of IR spectra of deprotonated serine with classical and thermostated ring polymer molecular dynamics simulations

Here we report the vibrational spectra of deprotonated serine calculated from the classical molecular dynamics (MD) simulations and thermostated ring-polymer molecular dynamics (TRPMD) simulation with third-order density-functional tight-binding. In our earlier study [Inakollu and Yu, "A systematic benchmarking of computational vibrational spectroscopy with DFTB3: Normal mode analysis and fast Fourier transform dipole autocorrelation function," J. Comput. Chem. 39, 2067 (2018)] of deprotonated serine, we observed a significant difference in the vibrational spectra with the classical MD simulations compared to the infrared multiple photon dissociation spectra. It was postulated that this is due to neglecting the nuclear quantum effects (NQEs). In this work, NQEs are considered in spectral calculation using the TRPMD simulations. With the help of potential of mean force calculations, the conformational space of deprotonated serine is analyzed and used to understand the difference in the spectra of classical MD and TRPMD simulations at 298.15 and 100 K. The high-frequency vibrational bands in the spectra are characterized using Fourier transform localized vibrational mode (FT-νN AC) and interatomic distance histograms. At room temperature, the quantum effects are less significant, and the free energy profiles in the classical MD and the TRPMD simulations are very similar. However, the hydrogen bond between the hydroxyl-carboxyl bond is slightly stronger in TRPMD simulations. At 100 K, the quantum effects are more prominent, especially in the 2600-3600 cm-1, and the free energy profile slightly differs between the classical MD and TRPMD simulations. Using the FT-νN AC and the interatomic distance histograms, the high-frequency vibrational bands are discussed in detail.