Quasi-classical trajectory analysis with isometric feature mapping and locally linear embedding: deep insights into the multichannel reaction on an NH3+(4A) potential energy surface

Literature Information

Publication Date 2020-06-29
DOI 10.1039/D0CP01941K
Impact Factor 3.676
Authors

Weiliang Shi, Tian Jia, Anyang Li


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Abstract

From the perspective of data analysis, finding and determining reaction paths from the quasi-classical trajectories of a polyatomic reaction system are equivalent to finding low-dimensional manifolds embedded in a high-dimensional space. Two manifold learning methods, isometric feature mapping and locally linear embedding, are applied to the analysis of reaction trajectories, which are calculated by the quasi-classical trajectory approach on a newly developed accurate quartet state NH3+(4A) potential energy surface for a multichannel reaction NH+ + H2 → N + H3+/NH2+ + H. The results show that isometric feature mapping can clearly identify different reaction paths from the reactive trajectories, and the locally linear embedding is better for the classification of non-reactive trajectories, and both of them facilitate quantitative analysis. With the help of trajectory analysis, the competition between the two H-atom abstraction reactions can be attributed to two different capture paths.

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DOI: 10.1039/C4PY90067G

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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