Photoinduced Desorption Dynamics of CO from Pd(111): A Neural Network Approach

Understanding the Photoinduced Desorption and Oxidation of CO on Ru(0001) Using a Neural Network Potential Energy Surface

The study of ultrafast photoinduced dynamics of adsorbates on metal surfaces requires thorough investigation of laser-excited electrons and, in many cases, the highly excited surface lattice. While ab initio molecular dynamics with electronic friction and thermostats (Te, Tl)-AIMDEF addresses such complex modeling, it imposes severe computational costs, hindering quantitative comparison with experimental desorption probabilities. In order to bypass this limitation, we utilize the embedded atom neural network method to construct a potential energy surface (PES) for the coadsorption of CO and O on Ru(0001). Our results demonstrate that this PES not only reproduces the short-time ab initio dynamics but is also able to yield statistically significant data for long lasting trajectories that correlate well with experimental findings. Furthermore, the analysis of the laser-induced dynamics reveals the existence of a dynamic trapping state that acts as a precursor for CO desorption, and it is not observed under thermal conditions. Altogether, our results validate the underlying theoretical framework, providing robust support for the description of not only the photoinduced desorption but also the oxidation of CO in terms of nonequilibrated but thermal hot electrons and phonons.

Multicoverage Study of Femtosecond Laser-Induced Desorption of CO from Pd(111)

We study the strong coverage dependence of the femtosecond laser-induced desorption of CO from Pd(111) using molecular dynamics simulations that consistently include the effect of the laser-induced hot electrons on both the adsorbates and surface atoms. Adiabatic forces are obtained from a multicoverage neural network potential energy surface that we construct using data from density functional theory calculations for 0.33 and 0.75 monolayer (ML). Our molecular dynamics simulations performed for these two trained coverages and an additional intermediate coverage of 0.60 ML reproduce well the peculiarities of the experimental findings. The performed simulations also permit us to disentangle the relative role played by the excited electrons and phonons on the desorption process and discover interesting properties of the reaction dynamics as the relevance that the precursor physisorption well acquires during the dynamics as coverage increases.

Modeling Equilibration Dynamics of Hyperthermal Products of Surface Reactions Using Scalable Neural Network Potential with First-Principles Accuracy

Equilibration dynamics of hot oxygen atoms following the dissociation of O2 on Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations based on a scalable neural network potential enabling first-principles description of the interaction of O2 and O interacting with variable Pd supercells. By analyzing hundreds of trajectories with appropriate initial sampling, the measured distance distribution of equilibrated atom pairs on Pd(111) is well reproduced. However, our results on Pd(100) suggest that the ballistic motion of hot atoms predicted previously is a rare event under ideal conditions, while initial molecular orientation and surface thermal fluctuation could significantly affect the overall postdissociation dynamics. On both surfaces, dissociated hyperthermal oxygen atoms primarily locate their nascent positions and experience a similar random walk motion nearby.

Disentangling the role of electrons and phonons in the photoinduced CO desorption and CO oxidation on (O,CO)-Ru(0001)

The role played by electronic and phononic excitations in the femtosecond laser induced desorption and oxidation of CO coadsorbed with O on Ru(0001) is investigated using ab initio molecular dynamics with electronic friction. To this aim, simulations that account for both kind of excitations and that only consider electronic excitations are performed. Results for three different surface coverages are obtained. We unequivocally demonstrate that CO desorption is governed by phononic excitations. In the case of oxidation the low statistics does not allow to give a categorical answer. However, the analysis of the adsorbates kinetic energy gain and displacements strongly suggest that phononic excitations and surface distortion also play an important role in the oxidation process.

Investigating the Eley-Rideal recombination of hydrogen atoms on Cu (111) via a high-dimensional neural network potential energy surface

As a prototypical system for studying the Eley-Rideal (ER) mechanism at the gas-surface interface, the reaction between incident H/D atoms and pre-covered D/H atoms on Cu (111) has attracted much experimental and theoretical interest. Detailed final state-resolved experimental data have been available for about thirty-years, leading to the discovery of many interesting dynamical features. However, previous theoretical models have suffered from reduced-dimensional approximations and/or omitting energy transfer to surface phonons and electrons, or the high cost of on-the-fly ab initio molecular dynamics, preventing quantitative comparisons with experimental data. Herein, we report the first high-dimensional neural network potential (NNP) for this ER reaction based on first-principles calculations including all molecular and surface degrees of freedom. Thanks to the high efficiency of this NNP, we are able to perform extensive quasi-classical molecular dynamics simulations with the inclusion of the excitation of low-lying electron-hole pairs (EHPs), which generally yield good agreement with various experimental results. More importantly, the isotopic and/or EHP effects in total reaction cross-sections and distributions of the product energy, scattering angle, and individual ro-vibrational states have been more clearly shown and discussed. This study sheds valuable light on this important ER prototype and opens a new avenue for further investigations of ER reactions using various initial conditions, surface temperatures, and coverages in the future.

Why Ultrafast Photoinduced CO Desorption Dominates over Oxidation on Ru(0001)

CO oxidation on Ru(0001) is a long-standing example of a reaction that, being thermally forbidden in ultrahigh vacuum, can be activated by femtosecond laser pulses. In spite of its relevance, the precise dynamics of the photoinduced oxidation process as well as the reasons behind the dominant role of the competing CO photodesorption remain unclear. Here we use ab initio molecular dynamics with electronic friction that account for the highly excited and nonequilibrated system created by the laser to investigate both reactions. Our simulations successfully reproduce the main experimental findings: the existence of photoinduced oxidation and desorption, the large desorption to oxidation branching ratio, and the changes in the O K-edge X-ray absorption spectra attributed to the initial stage of the oxidation process. Now, we are able to monitor in detail the ultrafast CO desorption and CO oxidation occurring in the highly excited system and to disentangle what causes the unexpected inertness to the otherwise energetically favored oxidation.

NQCDynamics.jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase

Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-dimensional and dissipative systems are crucial for the prediction of chemical dynamics in the condensed phase. To facilitate effective development, code sharing, and uptake of newly developed dynamics methods, it is important that software implementations can be easily accessed and built upon. Using the Julia programming language, we have developed the NQCDynamics.jl package, which provides a framework for established and emerging methods for performing semiclassical and mixed quantum-classical dynamics in the condensed phase. The code provides several interfaces to existing atomistic simulation frameworks, electronic structure codes, and machine learning representations. In addition to the existing methods, the package provides infrastructure for developing and deploying new dynamics methods, which we hope will benefit reproducibility and code sharing in the field of condensed phase quantum dynamics. Herein, we present our code design choices and the specific Julia programming features from which they benefit. We further demonstrate the capabilities of the package on two examples of chemical dynamics in the condensed phase: the population dynamics of the spin-boson model as described by a wide variety of semiclassical and mixed quantum-classical nonadiabatic methods and the reactive scattering of H₂ on Ag(111) using the molecular dynamics with electronic friction method. Together, they exemplify the broad scope of the package to study effective model Hamiltonians and realistic atomistic systems.

REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems

In this work, we present a general purpose deep neural network package for representing energies, forces, dipole moments, and polarizabilities of atomistic systems. This so-called recursively embedded atom neural network model takes advantages of both the physically inspired atomic descriptor based neural networks and the message-passing based neural networks. Implemented in the PyTorch framework, the training process is parallelized on both the central processing unit and the graphics processing unit with high efficiency and low memory in which all hyperparameters can be optimized automatically. We demonstrate the state-of-the-art accuracy, high efficiency, scalability, and universality of this package by learning not only energies (with or without forces) but also dipole moment vectors and polarizability tensors in various molecular, reactive, and periodic systems. An interface between a trained model and LAMMPs is provided for large scale molecular dynamics simulations. We hope that this open-source toolbox will allow for future method development and applications of machine learned potential energy surfaces and quantum-chemical properties of molecules, reactions, and materials.