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Finding Chemical Reaction Paths with a Multilevel Preconditioning Protocol.

Kale S, Sode O, Weare J, Dinner AR - J Chem Theory Comput (2014)

Bottom Line: Chem.Phys. 2014, 140, 184114) can be used to accelerate quantum-chemical string calculations.The approach also shows promise for free energy calculations when thermal noise can be controlled.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States.

ABSTRACT

Finding transition paths for chemical reactions can be computationally costly owing to the level of quantum-chemical theory needed for accuracy. Here, we show that a multilevel preconditioning scheme that was recently introduced (Tempkin et al. J. Chem. Phys. 2014, 140, 184114) can be used to accelerate quantum-chemical string calculations. We demonstrate the method by finding minimum-energy paths for two well-characterized reactions: tautomerization of malonaldehyde and Claissen rearrangement of chorismate to prephanate. For these reactions, we show that preconditioning density functional theory (DFT) with a semiempirical method reduces the computational cost for reaching a converged path that is an optimum under DFT by several fold. The approach also shows promise for free energy calculations when thermal noise can be controlled.

No MeSH data available.


Related in: MedlinePlus

MOFJ plot for MPP hydrolysis. Abscissais Pβ-Obr distance, ordinate is Pβ-On distance.Red and blue string pairs indicate first and last R and ML paths includedin averaging potential energies. Insets are representative snapshotsfrom the final ML path. Initial path is in black. Contoured landscapeis from well-tempered metadynamics50 (WTM)to provide information about the energy landscape local to the finalstring pathway. WTM setup uses 10 walkers51 that are restarted every 500 MD steps from the final points of R-onlyimages. Gaussian hills of height 1 kcal/mol are deposited every 50steps on the space spanned by nonprotonic CVs. For WTM, ΔT is 5000 K. To reduce water evaporation in WTM, reflectiveboundaries are applied on all CVs at 5.0 Å. Walkers are integratedusing the same MD time step and thermostat as in string trajectories.Over ∼51 000 hills are collected to reproduce the freeenergy surface. These simulations show that the transition state regionis fairly flat, so that the variations in the paths are likely toreflect thermal fluctuations. Free energy contours are spaced by 5kcal/mol.
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fig12: MOFJ plot for MPP hydrolysis. Abscissais Pβ-Obr distance, ordinate is Pβ-On distance.Red and blue string pairs indicate first and last R and ML paths includedin averaging potential energies. Insets are representative snapshotsfrom the final ML path. Initial path is in black. Contoured landscapeis from well-tempered metadynamics50 (WTM)to provide information about the energy landscape local to the finalstring pathway. WTM setup uses 10 walkers51 that are restarted every 500 MD steps from the final points of R-onlyimages. Gaussian hills of height 1 kcal/mol are deposited every 50steps on the space spanned by nonprotonic CVs. For WTM, ΔT is 5000 K. To reduce water evaporation in WTM, reflectiveboundaries are applied on all CVs at 5.0 Å. Walkers are integratedusing the same MD time step and thermostat as in string trajectories.Over ∼51 000 hills are collected to reproduce the freeenergy surface. These simulations show that the transition state regionis fairly flat, so that the variations in the paths are likely toreflect thermal fluctuations. Free energy contours are spaced by 5kcal/mol.

Mentions: As mentioned above, we use the PM6 force field as the referenceand precondition it with low-temperature (T = 10K) PM3. As can be seen in Figure 12, the PM6reaction path consists of protonation of a phosphate by the catalyticwater, followed by attack by the nucleophilic water and neutralizationby transferring a proton between the waters (Figure 12). The ML simulations reprise this path (Figure 12), reaching it in about 3-fold fewer iterations(Figure 13).


Finding Chemical Reaction Paths with a Multilevel Preconditioning Protocol.

Kale S, Sode O, Weare J, Dinner AR - J Chem Theory Comput (2014)

MOFJ plot for MPP hydrolysis. Abscissais Pβ-Obr distance, ordinate is Pβ-On distance.Red and blue string pairs indicate first and last R and ML paths includedin averaging potential energies. Insets are representative snapshotsfrom the final ML path. Initial path is in black. Contoured landscapeis from well-tempered metadynamics50 (WTM)to provide information about the energy landscape local to the finalstring pathway. WTM setup uses 10 walkers51 that are restarted every 500 MD steps from the final points of R-onlyimages. Gaussian hills of height 1 kcal/mol are deposited every 50steps on the space spanned by nonprotonic CVs. For WTM, ΔT is 5000 K. To reduce water evaporation in WTM, reflectiveboundaries are applied on all CVs at 5.0 Å. Walkers are integratedusing the same MD time step and thermostat as in string trajectories.Over ∼51 000 hills are collected to reproduce the freeenergy surface. These simulations show that the transition state regionis fairly flat, so that the variations in the paths are likely toreflect thermal fluctuations. Free energy contours are spaced by 5kcal/mol.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4263463&req=5

fig12: MOFJ plot for MPP hydrolysis. Abscissais Pβ-Obr distance, ordinate is Pβ-On distance.Red and blue string pairs indicate first and last R and ML paths includedin averaging potential energies. Insets are representative snapshotsfrom the final ML path. Initial path is in black. Contoured landscapeis from well-tempered metadynamics50 (WTM)to provide information about the energy landscape local to the finalstring pathway. WTM setup uses 10 walkers51 that are restarted every 500 MD steps from the final points of R-onlyimages. Gaussian hills of height 1 kcal/mol are deposited every 50steps on the space spanned by nonprotonic CVs. For WTM, ΔT is 5000 K. To reduce water evaporation in WTM, reflectiveboundaries are applied on all CVs at 5.0 Å. Walkers are integratedusing the same MD time step and thermostat as in string trajectories.Over ∼51 000 hills are collected to reproduce the freeenergy surface. These simulations show that the transition state regionis fairly flat, so that the variations in the paths are likely toreflect thermal fluctuations. Free energy contours are spaced by 5kcal/mol.
Mentions: As mentioned above, we use the PM6 force field as the referenceand precondition it with low-temperature (T = 10K) PM3. As can be seen in Figure 12, the PM6reaction path consists of protonation of a phosphate by the catalyticwater, followed by attack by the nucleophilic water and neutralizationby transferring a proton between the waters (Figure 12). The ML simulations reprise this path (Figure 12), reaching it in about 3-fold fewer iterations(Figure 13).

Bottom Line: Chem.Phys. 2014, 140, 184114) can be used to accelerate quantum-chemical string calculations.The approach also shows promise for free energy calculations when thermal noise can be controlled.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States ; Department of Chemistry, James Franck Institute, Institute for Biophysical Dynamics, Computation Institute, Department of Statistics, University of Chicago , Chicago, Illinois 60637, United States.

ABSTRACT

Finding transition paths for chemical reactions can be computationally costly owing to the level of quantum-chemical theory needed for accuracy. Here, we show that a multilevel preconditioning scheme that was recently introduced (Tempkin et al. J. Chem. Phys. 2014, 140, 184114) can be used to accelerate quantum-chemical string calculations. We demonstrate the method by finding minimum-energy paths for two well-characterized reactions: tautomerization of malonaldehyde and Claissen rearrangement of chorismate to prephanate. For these reactions, we show that preconditioning density functional theory (DFT) with a semiempirical method reduces the computational cost for reaching a converged path that is an optimum under DFT by several fold. The approach also shows promise for free energy calculations when thermal noise can be controlled.

No MeSH data available.


Related in: MedlinePlus