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Open access Jul 2026

Coverage-Dependent Free-Energy Bottlenecks in CO Adsorption and Desorption on Ru(0001)

The adsorption and desorption of gas-phase molecules on solid surfaces are elementary steps in heterogeneous catalysis. However, capturing these dynamics under finite-temperature and coverage-dependent conditions remains challenging because static models cannot fully describe the coupled motion of adsorbates and surface atoms. In this study, we investigate the adsorption/desorption dynamics of CO on Ru(0001) by combining machine learning potential energy surfaces (ML-PESs) with umbrella sampling molecular dynamics (US-MD). A 2 × 2 × 5 Ru(0001) model containing one CO molecule (0.250 monolayer, ML) is used to analyze temperature-dependent potential of mean force (PMF) profiles, local free-energy landscapes, and molecular orientation distributions. To clarify coverage and finite-size effects, enlarged 4 × 4 × 5 Ru(0001) models containing 1, 4, and 8 CO molecules are used to cover 0.063, 0.250, and 0.500 ML, respectively, while 20 × 20 × 5 Ru(0001) models containing 100 and 200 CO molecules are further examined at 0.250 and 0.500 ML using a fine-tuned DPA-2 potential. The PMF profiles show that CO adsorption is nearly barrierless at low coverage, whereas an adsorption-side free-energy bottleneck becomes most clearly resolved under crowded high-coverage conditions. This bottleneck arises from the cooperative effect of configurational/rotational entropy loss and lateral CO–CO repulsion, with finite-size periodicity and surface coverage modulating the strength of entropic confinement in the PMF profile. Comparison between rigid and relaxed 2 × 2 × 5 surface models further shows that neglecting lattice motion qualitatively preserves the overall temperature-dependent PMF trend but suppresses the small adsorption-side bottleneck relative to the relaxed-surface model. These results highlight the coupled roles of coverage, entropy, lateral adsorbate interactions, and surface lattice degrees of freedom in CO/Ru(0001) gas–surface dynamics.

Jin He, Mingjun Yang, Zhe-Ning Chen et al. · 0 citations
Open access Jul 2026

Improvements in chemical reaction pathway exploration algorithms and dataset generation

Chemical reaction networks provide a comprehensive framework for understanding complex reaction systems, in which reaction path exploration is a critical component. In this study, molecular structures are represented as bond-electron matrices, and reaction candidates are systematically enumerated through matrix transformations. Starting from more than 1,000 reactant molecules, diverse reaction pathways were generated and validated using DFT calculations, resulting in OrgReact, a dataset comprising 9,649 reactions. The dataset includes reactant, product, and transition-state structures, together with associated energetic information, and is intended to support data-driven studies of organic reaction pathways and machine learning models for molecular energies and forces.

Zhaojia Dong, Hanwen Zhang, Bowen Li et al. · 0 citations

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