Can semi-empirical models describe HCl dissociation in water?

Arillo-Flores, O. I.; Ruiz‐López, Manuel F.; Bernal-Uruchurtu, Margarita I.

doi:10.1007/s00214-007-0280-z

Cited by 24 publications

(22 citation statements)

References 44 publications

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“…Re-optimization with CAM-B3LYP, BHHLYP and HFLYP (each using the def2-TZVPD basis set) also predicts a dissociated cluster, whereas re-optimization at BLYP/def2-TZVPD, B3LYP/def2-TZVPD and RIMP2/aug-cc-pVTZ levels of theory reverts back to the undissociated species (X 3u 4 ). Previous studies have reported this cluster both in its dissociated 29 and undissociated 17,31,34,77,81 form suggesting that the existence of C 3d 6 is very sensitive to the chosen computational model.…”

Section: Almo-edamentioning

confidence: 93%

Dissociation of HCl in water nanoclusters: an energy decomposition analysis perspective

Zech

Head‐Gordon

2021

Phys. Chem. Chem. Phys.

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Section: Almo-edamentioning

confidence: 93%

Dissociation of HCl in water nanoclusters: an energy decomposition analysis perspective

Zech

Head‐Gordon

2021

Phys. Chem. Chem. Phys.

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“…To assess the difference in using either a full diagonalization scheme or the D&C method, we have performed three different SEBOMD simulations of liquid water in the NVT ensemble at 300K using the PM3-PIF hamiltonian. 52,[58][59][60][61][62] The slow simulation corresponds to our previously defined slow scheme: dpmax= 10 −7 a.u., ∆t = 0.5fs. It is 40 ps long and can serve as a reference simulation.…”

Section: Standard Diagonalization Vs Divide and Conquer: The Example Omentioning

confidence: 99%

“…Currently available NDDO hamiltonians are MNDO, 50 AM1, 51 PM3, 52 RM1, 53 PM3/PDDG, 54 AM1/d-PhoT, 55 and AM1/d-CB1. 56 MAIS 57,58 and PIF [58][59][60][61][62] corrections to the PM3 Hamiltonian are also provided. SEBOMD simulations can be run with sander using usual thermodynamical ensembles: NVE, NVT, or NPT.…”

Section: Introductionmentioning

confidence: 99%

Semi-Empirical Born–Oppenheimer Molecular Dynamics (SEBOMD) within the Amber Biomolecular Package

Marion

Gökcan

Monard

2018

J. Chem. Inf. Model.

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Semiempirical quantum methods from the Neglect of Differential Diatomic Overlap (NDDO) family such as MNDO, AM1, or PM3 are fast albeit approximate quantum methods. By combining them with linear scaling methods like the Divide & Conquer (D&C) method, it is possible to quickly evaluate the energy of systems containing hundreds to thousands of atoms. We here present our implementation in the Amber biomolecular package of a SEBOMD module that provides a way to run SemiEmpirical Born-Oppenheimer Molecular Dynamics. At each step of a SEBOMD molecular dynamics, a fully converged SCF calculation is performed to obtain the semiempirical quantum potential energy of a molecular system encaged or not in periodic boundary conditions. We describe the implementation and the features of our SEBOMD implementation. We show the requirements to conserve the total energy in NVE simulations, and how to accelerate SCF convergence through density matrix extrapolation. Specific ways of handling periodic boundary conditions using mechanical embedding or electrostatic embedding through a tailored quantum Ewald summation is developed. The parallel performance of SEBOMD simulations using the D&C scheme are presented for liquid water systems of various sizes, and a comparison between the traditional full diagonalization scheme and the D&C approach for the reproduction of the structure of liquid water illustrates the potentiality of SE-BOMD to simulate molecular systems containing several hundreds of atoms for hundreds of picoseconds with a quantum mechanical potential in a reasonable amount of CPU time.

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“…To assess the difference in using either a full diagonalization scheme or the D&C method, we have performed three different SEBOMD simulations of liquid water in the NVT ensemble at 300K using the PM3-PIF hamiltonian. 52,[58][59][60][61][62] The slow simulation corresponds to our previously defined slow core, a first buffer of 6 Å and no second buffer. For all simulations, the NVT ensemble was enforced using Langevin dynamics as a thermostat with a weak coupling (collision frequency of 1 ps −1 ).…”

Section: Standard Diagonalization Vs Divide and Conquer: The Example Of Liquid Watermentioning

confidence: 99%

SemiEmpirical Born-Oppenheimer Molecular Dynamics (SEBOMD) Within the Amber Biomolecular Package

Marion¹,

Gökcan²,

Monard³

2018

Preprint

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<div>Semiempirical quantum methods from the Neglect of Differential Diatomic Overlap (NDDO) family such as MNDO, AM1, or PM3 are fast albeit approximate quantum methods. By combining them with linear scaling methods like the Divide & Conquer (D&C) method, it is possible to quickly evaluate the energy of systems containing hundreds to thousands of atoms. We here present our implementation in the Amber biomolecular package of a SEBOMD module that provides a way to run SemiEmpirical Born-Oppenheimer Molecular Dynamics. At each step of a SEBOMD molecular dynamics, a fully converged SCF calculation is performed to obtain the semiempirical quantum potential energy of a molecular system encaged or not in periodic boundary conditions. We describe the implementation and the features of our SEBOMD implementation. We show the equirements to conserve the total energy in NVE simulations, and how to accelerate SCF convergence through density matrix extrapolation. Specific ways of handling periodic boundary conditions using mechanical embedding or electrostatic embedding through a tailored quantum Ewald summation is developed. The parallel performance of SEBOMD simulations using the D&C scheme are presented for liquid water systems of various sizes, and a comparison between the traditional full diagonalization scheme and the D&C approach for the reproduction of the structure of liquid water illustrates the potentiality of SEBOMD to simulate molecular systems containing several hundreds of atoms for hundreds of picoseconds with a quantum mechanical potential in a reasonable amount of CPU time.</div>

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Can semi-empirical models describe HCl dissociation in water?

Cited by 24 publications

References 44 publications

Dissociation of HCl in water nanoclusters: an energy decomposition analysis perspective

Dissociation of HCl in water nanoclusters: an energy decomposition analysis perspective

Semi-Empirical Born–Oppenheimer Molecular Dynamics (SEBOMD) within the Amber Biomolecular Package

SemiEmpirical Born-Oppenheimer Molecular Dynamics (SEBOMD) Within the Amber Biomolecular Package

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