Permutation invariant polynomial neural network approach to fitting potential energy surfaces. III. Molecule-surface interactions

Bin Jiang, Hua Guo

The Journal of Chemical Physics 2014 article cited by 151

importance 4/10 solved Advanced Chemical Physics Studies computational chemistry stage: research

Problem

Constructing highly accurate potential energy surfaces for molecule-surface interactions was difficult, hindering simulation of reactions at surfaces.

Why it matters

Accurate molecule-surface PESs underpin prediction of surface chemistry relevant to catalysis and surface processes.

What the AI did

A permutation invariant polynomial-neural network (PIP-NN) method was extended to molecule-surface PESs using symmetry-adapted input functions, including permutationally invariant cross terms.

Results

Accuracy and efficiency demonstrated fitting a model PES for H2 + Cu(111) and DFT points for H2 + Ag(111); no numeric metrics in abstract

Impact

Provides a symmetry-adapted NN fitting method demonstrated on two molecule-surface systems.

Record

Venue	The Journal of Chemical Physics
Published	2014
Work type	article
Authors	Bin Jiang, Hua Guo
Citations	151 (OpenAlex, at import time)
References	71
Model or method	Permutation invariant polynomial-neural network (PIP-NN)
Organization	not stated in source
Primary source	https://doi.org/10.1063/1.4887363
Open access	closed
Keywords	neural network, potential energy surface, molecule-surface, permutation invariant, H2, DFT

Importance rating

4 / 10

real but narrower advance

Accurate molecule-surface PESs underpin surface chemistry/catalysis prediction, a foundational area, but abstract only demonstrates method on two systems with no scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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