Vibrational mode-specific dynamics of the F− + CH3CH2Cl multi-channel reaction

Tajti, Viktor; Czakó, Gábor

doi:10.1039/d2cp00685e

Cited by 17 publications

(37 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Because of the very steep scaling of CCSD(T) theory (N 7 , where N is the number of basis functions), the Δ-ML approach is a method that uses a small number (several hundreds to thousand) of CCSD(T) energies to correct a PES based on DFT electronic energies and gradients. For systems with more than 10 atoms, it becomes arduous job to compute CCSD(T) energies for a thousand geometries using a Dunning basis.…”

Section: ■ Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Bowman

Qu²,

Conte

et al. 2022

J. Chem. Theory Comput.

View full text Add to dashboard Cite

There has been great progress in developing machinelearned potential energy surfaces (PESs) for molecules and clusters with more than 10 atoms. Unfortunately, this number of atoms generally limits the level of electronic structure theory to less than the "gold standard" CCSD(T) level. Indeed, for the well-known MD17 dataset for molecules with 9−20 atoms, all of the energies and forces were obtained with DFT calculations (PBE). This Perspective is focused on a Δmachine learning method that we recently proposed and applied to bring DFT-based PESs to close to CCSD(T) accuracy. This is demonstrated for hydronium, N-methylacetamide, acetyl acetone, and ethanol. For 15atom tropolone, it appears that special approaches (e.g., molecular tailoring, local CCSD(T)) are needed to obtain the CCSD(T) energies. A new aspect of this approach is the extension of Δ-machine learning to force fields. The approach is based on many-body corrections to polarizable force field potentials. This is examined in detail using the TTM2.1 water potential. The corrections make use of our recent CCSD(T) datasets for 2-b, 3-b, and 4-b interactions for water. These datasets were used to develop a new fully ab initio potential for water, termed q-AQUA.

show abstract

Section: ■ Introductionmentioning

confidence: 99%

“…5 Recent reactive CCSD(T)-based PESs from Czako and coworkers for several nine-atom reactions are close seconds. 6−8 Of course, there are good reasons for the DFT/CCSD(T) dichotomy, the major one being that DFT scales as O(N 3 ) whereas CCSD(T) scales as O(N 7 ), where N is the size of the system.…”

Section: ■ Introductionmentioning

confidence: 99%

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Bowman

Qu²,

Conte

et al. 2022

J. Chem. Theory Comput.

View full text Add to dashboard Cite

show abstract

“…The final example is the 9-atom reaction F -+ CH 3 CH 2 Cl. The details of an MLP for this reaction were recently reported 57 and used in joint theory (QCT) and experimental study. 58 The PES was fit to 35 474 CCSD(T)-F12b energies.…”

Section: Gas-phase Reaction Potentialsmentioning

confidence: 99%

“…Details of the labels of the stationary points for the interested reader can be found in ref. 57. Clearly the dataset for this MLP is extended in energy and configuration space, as were the datasets shown above.…”

Section: Gas-phase Reaction Potentialsmentioning

confidence: 99%

See 1 more Smart Citation

The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials

Bowman,

Conte,

Nandi

et al. 2022

Preprint

View full text Add to dashboard Cite

There has been great progress in developing methods for machine-learned potential energy surfaces. There have also been important assessments of these methods by comparing so-called learning curves on datasets of electronic energies and forces, notably the MD17 database. The dataset for each molecule in this database generally consists of tens of thousands of energies and forces obtained from DFT direct dynamics at 500 K. We contrast the datasets from this database for three "small" molecules, ethanol, malonaldehyde, and glycine, with datasets we have generated with specific targets for the PESs in mind: a rigorous calculation of the zero-point energy and wavefunction, the tunneling splitting in malonaldehyde and in the case of glycine a description of all eight low-lying conformers. We found that the MD17 datasets are too limited for these targets. We also examine recent datasets for several PESs that describe small-molecule but complex chemical reactions. Finally, we introduce a new database,"QM-22", which contains datasets of molecules ranging from 4 to 15 atoms that extend to high energies and a large span of configurations.

show abstract

S_N2 versus E2 Competition of Cyclic Ethers

Hansen,

Vermeeren,

Zijderveld

et al. 2023

Chemistry A European J

View full text Add to dashboard Cite

We have quantum chemically studied the influence of ring strain on the competition between the two mechanistically different SN2 and E2 pathways using a series of archetypal ethers as substrate in combination with a diverse set of Lewis bases (F−, Cl−, Br−, HO−, H3CO−, HS−, H3CS−), using relativistic density functional theory at ZORA‐OLYP/QZ4P. The ring strain in the substrate is systematically increased on going from a model acyclic ether to a 6‐ to 5‐ to 4‐ to 3‐membered ether ring. We have found that the activation energy of the SN2 pathway sharply decreases when the ring strain of the system is increased, thus on going from large to small cyclic ethers, the SN2 reactivity increases. In contrast, the activation energy of the E2 pathway generally rises along this same series, that is, from large to small cyclic ethers. The opposing reactivity trends induce a mechanistic switch in the preferred reaction pathway for strong Lewis bases from E2, for large cyclic substrates, to SN2, for small cyclic substrates. Weak Lewis bases are unable to overcome the higher intrinsic distortivity of the E2 pathway and, therefore, always favor the less distortive SN2 reaction.

show abstract

Vibrational mode-specific dynamics of the F⁻ + CH₃CH₂Cl multi-channel reaction

Abstract: Quasi-classical trajectory computations on a high-level analytical potential energy surface reveal the mode-specific dynamics of the F− + CH3CH2Cl reaction.

Cited by 17 publications

References 60 publications

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Δ-Machine Learned Potential Energy Surfaces and Force Fields

The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials

S_N2 versus E2 Competition of Cyclic Ethers

Contact Info

Product

Resources

About

Vibrational mode-specific dynamics of the F− + CH3CH2Cl multi-channel reaction

Abstract: Quasi-classical trajectory computations on a high-level analytical potential energy surface reveal the mode-specific dynamics of the F− + CH3CH2Cl reaction.

Cited by 17 publications

References 60 publications

Δ-Machine Learned Potential Energy Surfaces and Force Fields

Δ-Machine Learned Potential Energy Surfaces and Force Fields

The MD17 Datasets from the Perspective of Datasets for Gas-Phase "Small" Molecule Potentials

SN2 versus E2 Competition of Cyclic Ethers

Contact Info

Product

Resources

About

Vibrational mode-specific dynamics of the F⁻ + CH₃CH₂Cl multi-channel reaction

S_N2 versus E2 Competition of Cyclic Ethers