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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

Convergence of MPP path optimization:R-only (red) and ML (blue).We measure convergence by the decrease of the R-model potential energyaveraged over all images. Orange indicates control R-only sequencestarting from the last ML point. Boxes show the plateau regions forthe most likely minimum energy paths. Inset shows an expanded viewnear convergence.
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fig13: Convergence of MPP path optimization:R-only (red) and ML (blue).We measure convergence by the decrease of the R-model potential energyaveraged over all images. Orange indicates control R-only sequencestarting from the last ML point. Boxes show the plateau regions forthe most likely minimum energy paths. Inset shows an expanded viewnear convergence.

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)

Convergence of MPP path optimization:R-only (red) and ML (blue).We measure convergence by the decrease of the R-model potential energyaveraged over all images. Orange indicates control R-only sequencestarting from the last ML point. Boxes show the plateau regions forthe most likely minimum energy paths. Inset shows an expanded viewnear convergence.
© Copyright Policy
Related In: Results  -  Collection

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

fig13: Convergence of MPP path optimization:R-only (red) and ML (blue).We measure convergence by the decrease of the R-model potential energyaveraged over all images. Orange indicates control R-only sequencestarting from the last ML point. Boxes show the plateau regions forthe most likely minimum energy paths. Inset shows an expanded viewnear convergence.
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