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

Zhu, Lingjun; Hu, Ce; Chen, Jialu; Jiang, Bin

doi:10.1039/d2cp05479e

Cited by 9 publications

(11 citation statements)

References 83 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fortunately, recent QCT calculations with electronic friction based on a multiadsorbate neural network potential suggested that these effects are not significant. 50 Nevertheless, the predicted vibrational state distribution remained too cold compared to the experiment. Apparently, the disagreement between theory and experiment in this aspect remains unsolved, and the origin is not clear.…”

Section: Product State Distributions Figure 3 Compares the Internal S...mentioning

confidence: 92%

“…One reason for using this empirical PES is to enable direct comparison of present single-adsorbate results with previous multiadsorbate QCT results, 54 which were obtained by the same PES. We also have a neural network-based PES at hand, 50 which, however, contains two adsorbates in the supercell, while the current QD implementation can only deal with the single-adsorbate case.…”

Section: Theorymentioning

confidence: 99%

“…These experiments investigating the energy transfer dynamics in ER reactions have stimulated many theoretical studies, − using both quantum dynamics (QD) − , and quasi-classical trajectory (QCT) methods. ,, Due to its low cost, the latter has been widely applied to a variety of ER systems including multiadsorbates and/or the lattice motion. ,,,− Additionally, the nonadiabatic effects have also been effectively included in the QCT framework by adding an electronic frictional force and a fluctuation force, reflecting the feedback of electronic bath to the nuclear motion within the electron friction theory. ,− It was found that electron–hole pair excitations reduce the ER reactivity to some extent but do not alter much the product state distribution. Despite this progress, the QCT method does not rigorously conserve the vibrational zero-point energy (ZPE), whose reliability has not yet been validated for an ER reaction against a high-dimensional QD method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Six-Dimensional Quantum and Quasi-Classical Dynamics of the Eley–Rideal Reaction of H(D) Atoms with D(H)-Covered Cu(111)

Xiong,

Jiang

2024

J. Phys. Chem. C

Self Cite

View full text Add to dashboard Cite

The recombination reaction between gaseous and adsorbed hydrogen atoms on the Cu(111) surface is a paradigmatic system for studying the Eley−Rideal (ER) mechanism at the gas-surface interface. Theoretically, this system has been mostly studied by the highly efficient quasi-classical trajectory (QCT) method, whose validity has, however, not been tested against more rigorous quantum dynamics (QD) calculations in high dimensionality. Here, based on our recent development of a six-dimensional (6D) fully coupled quantum wave packet method for studying the ER reaction, we present a comparative study between QD and QCT calculations in the H(D)+D(H)/Cu(111) ER reaction. We find that quasi-classical results show a similar magnitude of total reactivity and qualitatively consistent vibrational and rotational state distributions as quantum ones. However, QCT to some extent overestimates the vibrational excitation compared to QD, presumably due to the unavoidable leakage of the zero-point energy of the adsorbate into the product. Additionally, vibrational state-specific internal energy distributions predicted by QCT and QD show more remarkable discrepancies for higher vibrational states. These results provide a reliable benchmark for the capability of the QCT method in studying ER reactions. By the comparison with experiments, it is also suggested that to more accurately describe the ER reaction, the current QD model requires further developments to incorporate the hot-atom reaction channel.

show abstract

Section: Product State Distributions Figure 3 Compares the Internal S...mentioning

confidence: 92%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Six-Dimensional Quantum and Quasi-Classical Dynamics of the Eley–Rideal Reaction of H(D) Atoms with D(H)-Covered Cu(111)

Xiong,

Jiang

2024

J. Phys. Chem. C

Self Cite

View full text Add to dashboard Cite

show abstract

“…Reactive hydrogen chemistry at surfaces plays an important role in key heterogeneous catalysis processes such as the Haber–Bosch process , or methanol synthesis. − Copper is a particularly interesting metal to study in this context due to its widespread use as a catalyst in many chemical reactions. Consequently, the dissociation of hydrogen on Cu surfaces has become a benchmark problem for experimental − and theoretical − research.…”

Section: Introductionmentioning

confidence: 99%

“… 6 − 8 Copper is a particularly interesting metal to study in this context due to its widespread use as a catalyst in many chemical reactions. Consequently, the dissociation of hydrogen on Cu surfaces has become a benchmark problem for experimental 9 − 17 and theoretical 18 − 34 research.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities

Stark,

Westermayr,

Douglas-Gallardo

et al. 2023

J. Phys. Chem. C

View full text Add to dashboard Cite

The reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction design, but studying dynamics at surfaces is computationally challenging due to the complex electronic structure at interfaces and the high sensitivity of dynamics to reaction barriers. In addition, ab initio molecular dynamics, based on density functional theory, is too computationally demanding to accurately predict reactive sticking or desorption probabilities, as it requires averaging over tens of thousands of initial conditions. High-dimensional machine learning-based interatomic potentials are starting to be more commonly used in gas-surface dynamics, yet robust approaches to generate reliable training data and assess how model uncertainty affects the prediction of dynamic observables are not well established. Here, we employ ensemble learning to adaptively generate training data while assessing model performance with full uncertainty quantification (UQ) for reaction probabilities of hydrogen scattering on different copper facets. We use this approach to investigate the performance of two message-passing neural networks, SchNet and PaiNN. Ensemble-based UQ and iterative refinement allow us to expose the shortcomings of the invariant pairwise-distance-based feature representation in the SchNet model for gas-surface dynamics.

show abstract

Selectivities of Cu edges surface environment for C1 and C2 pathways

Sun,

Wu,

et al. 2024

Molecular Catalysis

View full text Add to dashboard Cite

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

Abstract: 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...

Cited by 9 publications

References 83 publications

Comparison of Six-Dimensional Quantum and Quasi-Classical Dynamics of the Eley–Rideal Reaction of H(D) Atoms with D(H)-Covered Cu(111)

Comparison of Six-Dimensional Quantum and Quasi-Classical Dynamics of the Eley–Rideal Reaction of H(D) Atoms with D(H)-Covered Cu(111)

Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities

Selectivities of Cu edges surface environment for C1 and C2 pathways

Contact Info

Product

Resources

About