Isomerization and Decomposition Reactions of Acetaldehyde Relevant to Atmospheric Processes from Dynamics Simulations on Neural Network-Based Potential Energy Surfaces
arXiv:2003.08171 · doi:10.1063/5.0008223
Abstract
Acetaldehyde (AA) isomerization (to vinylalcohol, VA) and decomposition (into either CO+CH and H+HCCO) is studied using a fully dimensional, reactive potential energy surface represented as a neural network (NN). The NN, trained on 432'399 reference structures from MP2/aug-cc-pVTZ calculations has a MAE of 0.0453 kcal/mol and an RMSE of 1.186 kcal/mol for a test set of 27'399 structures. For the isomerization process AA VA the minimum dynamical path implies that the C-H vibration, and the C-C-H (with H being the transferring H-atom) and the C-C-O angles are involved to surmount the 68.2 kcal/mol barrier. Using an excess energy of 93.6 kcal/mol - the energy available in the solar spectrum and sufficient to excite to the first electronically excited state - to initialize the molecular dynamics, no isomerization to VA is observed on the 500 ns time scale. Only with excess energies of 127.6 kcal/mol (including the zero point energy of the AA molecule), isomerization occurs on the nanosecond time scale. Given that collisional de-excitation at atmospheric conditions in the stratosphere occurs on the 100 ns time scale, it is concluded that formation of VA following photoexcitation of AA from actinic photons is unlikely. This also limits the relevance of this reaction pathway to be a source for formic acid.
References in corpus (4)
- Neural Message Passing for Quantum Chemistry
- ANI-1: An extensible neural network potential with DFT accuracy at force field computational cost
- PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges
- Reactive Atomistic Simulations of Diels-Alder Reactions: the Importance of Molecular Rotations
Cited by in corpus (11)
- Machine Learning Force Fields
- Permutationally Invariant, Reproducing Kernel-Based Potential Energy Surfaces for Polyatomic Molecules: From Formaldehyde to Acetone
- Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water
- ML Models of Vibrating HCO: Comparing Reproducing Kernels, FCHL and PhysNet
- Perspective: Non-conventional Force Fields for Applications in Spectroscopy and Chemical Reaction Dynamics
- PhysNet Meets CHARMM: A Framework for Routine Machine Learning / Molecular Mechanics Simulations
- Impact of the characteristics of quantum chemical databases on machine learning predictions of tautomerization energies
- Is there any linkage between interstellar aldehyde and alcohol?
- Learning intermolecular forces at liquid-vapor interfaces
- Transfer Learned Potential Energy Surfaces: Accurate Anharmonic Vibrational Dynamics and Dissociation Energies for the Formic Acid Monomer and Dimer
- Quantitative Molecular Simulations