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Merge pull request #80 from EfremBraun/efrembraun
Minor typo fixes
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paper/basic_training.tex

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@@ -310,6 +310,10 @@ \subsubsection{Key concepts}
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In this type of non-equilibrium situation, the distribution of configurations will not follow a Boltzmann factor distribution.
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Specialized simulation approaches are available to study such systems~\cite{Chong:2017:CurrentOpinioninStructuralBiology, Zuckerman:2017:AnnuRevBiophys} but they are not beginner-friendly.
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Non-equilibrium molecular concepts pertinent to cell biology have been discussed at an introductory level (e.g. \url{http://www.physicallensonthecell.org/}).
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Notably, many experiments are conducted at non-equilibrium conditions; for example, membrane diffusion coefficients are commonly measured by setting up a concentration gradient across the membrane and measuring the flux.
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It can be tempting to the beginner to setup an MD simulation in the same manner as such an experiment.
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However, maintaining non-equilibrium conditions is typically more complicated in an MD simulation than in an experiment as large reservoirs are commonly required.
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Frequently there exist equilibrium methods that can be used to obtain the same or similar information as a non-equilibrium experiment; users should seek to obtain familiarity with such methods before choosing to conduct a non-equilibrium MD simulation.
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\subsubsection{Books}
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For all-atom simulations with constraints on the high-frequency bonds, timesteps can be commonly increased to 2 fs; coarse-grained simulations with particles of higher mass and smaller force constants can have much larger timesteps.
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After choosing a timestep, a test simulation should be run in the microcanonical ensemble to ensure that the choice of timestep yields dynamics that conserve energy.
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Methods also exist to increase the timestep beyond the limit imposed by the system's highest-frequency motion.
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Many such enhanced timestepping algorithms exist such as multiple-timestep methods which separately integrate high-frequency and low-frequency motion and schemes which repartition atomic masses to decrease the highest-frequency motion seen in the system\cite{Berne:1999:Molecular,Hopkins:2015:JCTC:Long}.
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Some examples of these enhanced timestepping algorithms include multiple-timestep methods which separately integrate high-frequency motion from low-frequency motion and schemes which repartition atomic masses to decrease the highest-frequency motion seen in the system\cite{Berne:1999:Molecular,Hopkins:2015:JCTC:Long}.
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\subsection{Long range electrostatics}
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However, here, because we use point charge electrostatics, $\rho$ is a set of delta functions.
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The Ewald method is based on (temporarily) replacing the point charge distributions by smooth charge distributions in order to apply existing numerical techniques to solve this partial differential equation (PDE).
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The most common smooth function used in Ewald method is the Gaussian distribution, although other distributions have been used as well.
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The most common smooth function used in the Ewald method is the Gaussian distribution, although other distributions have been used as well.
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Thus the overall charge distribution is divided into a short-range or ``direct space'' component ($\rho^{sr}$) involving the original point charges screened by the Gaussian-distributed charge of the same magnitude (Figure~\ref{fig:screening}) but opposite sign, and a long-range component involving Gaussian-distributed charges of the original sign ($\rho^{lr}$).
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\begin{figure}[h]
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The potential due to long-range charge interactions does not decay rapidly, and thus requires consideration of all periodic copies.
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This would pose severe problems if calculated via direct summation, but the smoothness of the charge $\rho^{lr}$ (and hence potential ($\phi^{lr}$)) allows the use of fast PDE solvers.
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Specifically, while the sum is long-ranged in real space, taking the Fourier transform converts it into a sum in reciprocal space which is short-ranged in reciprocal space, damped by a factor $\exp{-k^2 \sigma^2/2}$ where $k$ is the reciprocal space vector and $\sigma$ is the width of the Gaussian.
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This would pose severe problems if calculated via direct summation, but the smoothness of the charge $\rho^{lr}$ (and hence potential ($\phi^{lr}$) allows the use of fast PDE solvers.
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Specifically, while the sum is long-ranged in real space, taking the Fourier transform converts it into a sum in reciprocal space which is short-ranged in reciprocal space, damped by a factor $\exp\left(-k^2 \sigma^2/2\right)$ where $k$ is the reciprocal space vector and $\sigma$ is the width of the Gaussian.
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The final term in Ewald summation is a so-called self term which gets subtracted out of the overall sum; it is calculated only once at the beginning of the simulation as it depends only on the charge magnitudes and not their positions.
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It also does not contribute to the force.
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\begin{checklist}{Determine handling of cutoffs}
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\begin{itemize}
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\item As a general rule, electrostatics are long-range enough that either the cutoff needs to be larger than the system size (for finite systems) or
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periodicity is needed along with full treatment of long-range electrostatics (Section~\ref{sec:classical_electrostatics} % changed section ref since the folloing does not currently exist (Section~\ref{lr_electrostatics})
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periodicity is needed along with full treatment of long-range electrostatics (Section~\ref{sec:classical_electrostatics}) % changed section ref since the folloing does not currently exist (Section~\ref{lr_electrostatics})
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\item Nonpolar interactions can often be safely treated with cutoffs of 1-1.5 nm as long as the system size is at least twice that, but long-range dispersion corrections may be needed (Section~\ref{sec:force_fields})
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\end{itemize}
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\end{checklist}

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