Neural network potential energy surface for the low temperature ring polymer molecular dynamics of the H2CO + OH reaction
Problem
Accurate low-temperature dynamics for the H2CO + OH reaction requires a potential energy surface, but existing symmetry-adapted neural network schemes suffer spurious long-range interactions.
Why it matters
Reliable rate constants are needed to compare with and interpret experimental measurements of this reaction.
What the AI did
A novel artificial neural network scheme, combined with a diabatic matrix partition, fits a PES that avoids spurious asymptotic interactions, enabling QCT and RPMD rate-constant calculations.
Results
QCT and RPMD rate constants show good agreement with available experimental data.
Impact
Provides a validated low-temperature PES and rate constants for the H2CO + OH reaction.
Record
| Venue | The Journal of Chemical Physics |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Pablo del Mazo‐Sevillano, Alfredo Aguado, Octavio Roncero |
| Citations | 25 (OpenAlex, at import time) |
| References | 91 |
| Model or method | novel neural network scheme (diabatic matrix plus NN many-body term) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1063/5.0044009 |
| Open access | bronze |
| Keywords | neural network PES, ring polymer molecular dynamics, rate constants, H2CO + OH, quasi-classical trajectories |
Importance rating
incremental / niche
Niche reaction dynamics study: reliable low-temperature rate constants for one reaction (H2CO + OH) to interpret experiments; no broader scale claimed.
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