First-principles mode-specific reaction dynamics

Czakó, Gábor; Gruber, Balázs; Papp, Dóra; Tajti, Viktor; Tasi, Domonkos A.; Yin, Cangtao

doi:10.1039/d4cp00417e

Cited by 6 publications

(1 citation statement)

References 128 publications

(382 reference statements)

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“…It is noteworthy that the increasing number of monomials and polynomials with the system size has a smaller impact on the PIP method compared to the PIP-NN method. , For a single fit, the PIP method has been applied to the same class of systems as the PIP-NN and FI-NN methods and even to larger systems, − where PIP-NN and FI-NN methods have not yet been applied. Generally, the neural network (NN) architecture is more complex and has more parameters than the linear regression used in the PIP method.…”

Section: Introductionmentioning

confidence: 99%

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Song,

2024

J. Phys. Chem. A

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The hydrogen abstraction reaction of OH + CH 3 OH plays a great role in combustion and atmospheric and interstellar chemistry and has been extensively studied theoretically and experimentally. Theoretically, the numerical gradients with respect to the Cartesian coordinates of atoms in molecular simulations on our recent potential energy surface (PES) for the title reaction trained using the permutationally invariant polynomial neural network (PIP-NN) approach hinder the extensive calculation because of the unaffordable computation cost. To address this issue, we in this work report a new full-dimensional accurate analytical PES for the title reaction using the fundamental invariant neural network (FI-NN) approach based on 140,192 points of the quality UCCSD(T)-F12a/ AVTZ. Besides, the spin−orbit (SO) corrections of OH in the entrance channel were determined at the level of complete active space self-consistent field with the AVTZ basis set. As a compromise between computational cost and efficiency, the Δ-machine learning approach was employed to construct the SO-corrected PES. Based on this new FI-NN PES with analytical forces, thermal rate coefficients and various dynamic properties, including the integral cross sections, the differential cross sections, and the product energy partitioning, were determined by running a total of 5.5 million trajectories. The use of analytical gradients of the FI-NN PES accelerated simulations and about 99% of computation cost was saved, compared to that for the PIP-NN PES with numerical gradients. Such a significant acceleration is achieved mainly by replacing PIPs with FIs.

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Section: Introductionmentioning

confidence: 99%

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Song,

2024

J. Phys. Chem. A

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Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Nandi,

Pandey,

Houston

et al. 2024

J. Chem. Theory Comput.

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Progress in machine learning has facilitated the development of potentials that offer both the accuracy of first-principles techniques and vast increases in the speed of evaluation. Recently, Δ-machine learning has been used to elevate the quality of a potential energy surface (PES) based on low-level, e.g., density functional theory (DFT) energies and gradients to close to the gold-standard coupled cluster level of accuracy. We have demonstrated the success of this approach for molecules, ranging in size from H 3 O + to 15-atom acetyl-acetone and tropolone. These were all done using the B3LYP functional. Here, we investigate the generality of this approach for the PBE, M06, M06-2X, and PBE0 + MBD functionals, using ethanol as the example molecule. Linear regression with permutationally invariant polynomials is used to fit both low-level and correction PESs. These PESs are employed for standard RMSE analysis for training and test data sets, and then general fidelity tests such as energetics of stationary points, normal-mode frequencies, and torsional potentials are examined. We achieve similar improvements in all cases. Interestingly, we obtained significant improvement over DFT gradients where coupled cluster gradients were not used to correct the low-level PES. Finally, we present some results for correcting a recent molecular mechanics force field for ethanol and comment on the possible generality of this approach.

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Time-Resolved Chemical Bonding Structure Evolution by Direct-Dynamics Chemical Simulations

Piris,

Lopez,

Ugalde

2024

J. Phys. Chem. Lett.

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Direct-dynamics simulations monitor atomic nuclei trajectories during chemical reactions, where chemical bonds are broken and new ones are formed. While they provide valuable information about the ongoing nuclear dynamics, the evolution of the chemical bonds is customarily overlooked, thus, hindering key information about the reaction mechanism. Here we examine the evolution of the chemical bonds for the three main mechanisms of the F − + CH 3 CH 2 Cl reaction using quasi-classical trajectories for the nuclei, and global natural orbitals for the electrons. Key findings include (i) bimolecular nucleophilic substitution (S N 2) resembles a one-step bond breaking and formation process; (ii) the elimination mechanisms (syn-and anti-E2) feature a sequential two-step process of proton abstraction and Cl − elimination; and (iii) the anti-E2 mechanism is slower, exhibits rebound effects, and gets activated by specific vibrational modes. This study highlights the importance of correctly describing and thoroughly analyzing the dynamical evolution of chemical bonds for chemical reaction mechanistic studies.

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First-principles mode-specific reaction dynamics

Abstract: Controlling the outcome of chemical reactions by exciting specific vibrational and/or rotational modes of the reactants is one of the major goals of modern reaction dynamics studies. In the present...

Cited by 6 publications

References 128 publications

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Time-Resolved Chemical Bonding Structure Evolution by Direct-Dynamics Chemical Simulations

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First-principles mode-specific reaction dynamics

Abstract: Controlling the outcome of chemical reactions by exciting specific vibrational and/or rotational modes of the reactants is one of the major goals of modern reaction dynamics studies. In the present...

Cited by 6 publications

References 128 publications

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH3OH Reaction with Analytical Forces

Δ-Machine Learning to Elevate DFT-Based Potentials and a Force Field to the CCSD(T) Level Illustrated for Ethanol

Time-Resolved Chemical Bonding Structure Evolution by Direct-Dynamics Chemical Simulations

Contact Info

Product

Resources

About

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces

Fundamental Invariant Neural Network (FI-NN) Potential Energy Surface for the OH + CH₃OH Reaction with Analytical Forces