Suggested edits to steps of an md simulation section

Author: JIMonroe

paper/basic_training.tex

@@ -571,14 +571,14 @@ \subsection{Main steps of a molecular dynamics simulation}
 
 \subsubsection{System preparation}
 
-System preparation focuses on preparing the starting state of the desired system for simulation with the desired simulation package, including building a starting structure, solvating (if necessary), applying a force field etc.
-Because this step differs so much depending on the composition of the system and what information is available about the starting structure, it is a step which varies a great deal depending on the type of system and each category may require unique tools.
+System preparation focuses on preparing the starting state of the desired system with an appropriate simulation package, including building a starting structure, solvating (if necessary), applying a force field, etc.
+Because this step differs so much depending on the composition of the system and what information is available about the starting structure, it is a step which varies a great deal depending on the nature of the system at hand and as a result may require unique tools.
 
 Given the variable nature of system preparation, it is highly recommended that best practices documents specific to this issue and to the type of system of interest be consulted.
 If such documents do not exist, considerable care should be exercised to determine best practices from the literature.
 
 Loosely speaking, system preparation can be thought of as consisting of two \emph{logical} components which are not necessarily consecutive or separate.
-One comprises building the configuration of the system in the desired chemical state, and the other, applying force field parameters.
+One comprises building the configuration of the system in the desired chemical state and the other applying force field parameters.
 
 For building systems, freely available tools for constructing systems are available and can be a reasonable option (though their mention here should not be taken as an endorsement that they necessarily encapsulate best practices).
 Examples include tools for constructing specific crystal structures, proteins, and lipid membranes, such as Moltemplate, Packmol, and Atomsk.
@@ -590,10 +590,10 @@ \subsubsection{System preparation}
 System preparation is arguably the most critical stage of a simulation and in many cases receives the least attention.
 Specifically, if your system preparation is flawed, such flaws may prove fatal.
 Potentially the worst possible outcome is if the prepared system is not what you intended (e.g. it contains incorrect molecules or protonation states) but is chemically valid and well described by your force field and thus proceeds without error through the remaining steps --- and in fact this is a  frequent outcome of problems in system preparation.
-It should not be assumed that if a system can proceed in a well-behaved manner through the other steps, it was necessarily prepared correctly; considerable care should be taken here.
+It should not be assumed that a system has been prepared correctly if it is well-behaved in subsequent equilibration steps; considerable care should be taken here.
 
 Assignment or development of force field parameters is also critical, but is outside the scope of this work.
-For our purposes here, we will assume you have already obtained or developed force field parameters suitable for your system of interest.
+For our purposes, we will assume you have already obtained or developed force field parameters suitable for your system of interest.
 
 \subsubsection{Minimization}
 
@@ -613,7 +613,11 @@ \subsubsection{Assignment of velocities}
 \subsubsection{Equilibration}
 
 Ultimately, we usually seek to run a simulation in a particular thermodynamic ensemble (e.g. the NVE or NVT ensemble) at a particular state point (e.g. target energy, temperature, and pressure) and collect data for analysis which is appropriate for those conditions and not biased depending on our starting conditions/configuration.
-This means that usually we need to invest simulation time in bringing the system to the appropriate state point and allowing it to essentially forget about its history and reach equilibrium (or pseudo-equilibrium -- for some systems, such as biomolecular systems, reaching true equilibrium may be impractical) before we begin retaining data for analysis.
+This means that usually we need to invest simulation time in bringing the system to the appropriate state point and allowing it to essentially forget about its history and reach equilibrium before we begin retaining data for analysis.
+In most systems, we are interested in sampling the most relevant (or most probable) configurations in the equilibrium ensemble of interest.
+Thus, while the system may rigorously sample the desired equilibrium ensemble, large free energy barriers, such as configurational relaxations of biomolecules, may kinetically prevent the sampling of the most relevant configurations within the simulation timescale.
+While relevant to the current discussion, this topic in its entirety is outside the current scope and is addressed more fully in another best practices document (\url{https://github.com/dmzuckerman/Sampling-Uncertainty}). 
+%JIM: Is this the right idea?  Totally ok if it isn't - you can change it back.  I just don't want people to get the sense that you absolutely cannot equilibrate a biomolecule or other complicated system.  Under certain assumptions, you most certainly can, it just becomes a matter of how much you trust the assumptions, force field, etc.  We all don't have Anton, so we all take a bit of blind-faith leap at some point.  There's always another kinetic barrier we haven't crossed (i.e. always a longer timescale we can't probe).
 
 The most straightforward portion of equilibrium is bringing the system to the target state point.
 Usually, even though velocities are assigned according to the correct distribution, a thermostat will still need to add or remove heat from the system as it approaches the correct partitioning of kinetic and potential energies.
@@ -654,14 +658,17 @@ \subsubsection{Equilibration}
 
 \subsubsection{Production}
 
-Once equilibration is complete, we may begin collecting data for analysis, and typically this phase is called ``production''.
+Once equilibration is complete, we may begin collecting data for analysis.
+Typically this phase is called ``production''.
 The main difference between equilibration and production is simply that in the production simulation, we plan to retain and analyze the collected data.
 Production must always be preceded by equilibration appropriate for the target production ensemble, and production data should never be collected immediately after a change in conditions (such as rescaling a box size, energy minimizing, or suddenly changing the temperature or pressure) except in very specific applications where this is the goal.
 
 For bookkeeping purposes, sometimes practitioners choose to discard some initial production data as additional equilibration; usually this is simply to allow additional equilibration time after a change in protocol (such as a switch from NVT to NPT), and the usual considerations for equilibration apply in such cases (see \citet{ShellNotes}, lecture on Computing Properties). 
 
 Analysis of production is largely outside the scope of this work, but requires considerable care in computing observables and assessing the uncertainty in any computed properties.
 Usually, analysis involves computing expectation values of particular observables, and a key consideration is to obtain \emph{converged} estimates of these properties --- that is, estimates that are based on adequate simulation data so that they no longer depend substantially on the length of the simulation which was run or on its initial conditions.
+This is closely related to the above discussion of equilibration.
+Depending on the relaxation timescales involved, one may only realize after analysis of a ``production'' trajectory that the system was still equilibrating in some sense.
 
 A separate Best Practices document addresses these critical issues of convergence and error analysis (\url{https://github.com/dmzuckerman/Sampling-Uncertainty}). 
 For more specific details on procedures and parameters used in production simulations, see the appropriate best practices document for the system of interest.