How Many Conformations Need to Be Sampled to Obtain Converged QM/MM Energies? the Curse of Exponential Averaging

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

Abstract

Combined quantum mechanical and molecular mechanical (QM/MM) calculations is a popular approach to study enzymatic reactions. They are often based on a set of minimized structures obtained on snapshots from a molecular dynamics simulation to include some dynamics of the enzyme. It has been much discussed how the individual energies should be combined to obtain a final estimate of the energy, but the current consensus seems to be to use an exponential average. Then, the question is how many snapshots are needed to reach a reliable estimate of the energy. In this paper, I show that the question can be easily be answered if it is assumed that the energies follow a Gaussian distribution. Then, the outcome can be simulated based on a single parameter, σ, the standard deviation of the QM/MM energies from the various snapshots, and the number of required snapshots can be estimated once the desired accuracy and confidence of the result has been specified. Results for various parameters are presented, and it is shown that many more snapshots are required than is normally assumed. The number can be reduced by employing a cumulant approximation to second order. It is shown that most convergence criteria work poorly, owing to the very bad conditioning of the exponential average when σ is large (more than ∼7 kJ/mol), because the energies that contribute most to the exponential average have a very low probability. On the other hand, σ serves as an excellent convergence criterion.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Teoretisk kemi
Originalspråkengelska
Sidor (från-till)5745-5752
Antal sidor8
TidskriftJournal of Chemical Theory and Computation
Volym13
Utgivningsnummer11
StatusPublished - 2017 nov 14
PublikationskategoriForskning
Peer review utfördJa

Nedladdningar

Ingen tillgänglig data