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# Line 62 | Line 62 | molecules\cite{smallStuff} to complex biological
62  
63   Molecular dynamics has developed into a valuable tool for studying the
64   phase behavior of systems ranging from small or simple
65 < molecules\cite{smallStuff} to complex biological
65 > molecules\cite{Matsumoto02andOthers} to complex biological
66   species.\cite{bigStuff} Many techniques have been developed in order
67   to investigate the thermodynamic properites of model substances,
68   providing both qualitative and quantitative comparisons between
# Line 74 | Line 74 | conditions.\cite{lotsOfWaterPapers} Many of these mode
74   Water has proven to be a challenging substance to depict in
75   simulations, and has resulted in a variety of models that attempt to
76   describe its behavior under a varying simulation
77 < conditions.\cite{lotsOfWaterPapers} Many of these models have been
78 < used to investigate important physical phenomena like phase
79 < transitions and the hydrophobic effect.\cite{evenMorePapers} With the
80 < advent of numerous differing models, it is only natural that attention
81 < is placed on the properties of the models themselves in an attempt to
82 < clarify their benefits and limitations when applied to a system of
83 < interest.\cite{modelProps} One important but challenging property to
84 < quantify is the free energy, particularly of the solid forms of
85 < water. Difficulty in these types of studies typically arises from the
86 < assortment of possible crystalline polymorphs that water that water
87 < adopts over a wide range of pressures and temperatures. There are
88 < currently 13 recognized forms of ice, and it is a challenging task to
89 < investigate the entire free energy landscape.\cite{Sanz04} Ideally,
90 < research is focused on the phases having the lowest free energy,
91 < because these phases will dictate the true transition temperatures and
92 < pressures for their respective model.
77 > conditions.\cite{Berendsen81,Jorgensen83,Bratko85,Berendsen87,Liu96,Mahoney00,Fennell04}
78 > Many of these models have been used to investigate important physical
79 > phenomena like phase transitions and the hydrophobic
80 > effect.\cite{evenMorePapers} With the advent of numerous differing
81 > models, it is only natural that attention is placed on the properties
82 > of the models themselves in an attempt to clarify their benefits and
83 > limitations when applied to a system of interest.\cite{modelProps} One
84 > important but challenging property to quantify is the free energy,
85 > particularly of the solid forms of water. Difficulty in these types of
86 > studies typically arises from the assortment of possible crystalline
87 > polymorphs that water that water adopts over a wide range of pressures
88 > and temperatures. There are currently 13 recognized forms of ice, and
89 > it is a challenging task to investigate the entire free energy
90 > landscape.\cite{Sanz04} Ideally, research is focused on the phases
91 > having the lowest free energy, because these phases will dictate the
92 > true transition temperatures and pressures for their respective model.
93  
94   In this paper, standard reference state methods were applied to the
95   study of crystalline water polymorphs in the low pressure regime. This
# Line 97 | Line 97 | themselves\cite{nucleationStudies}; however, the cryst
97   arrived at through crystallization of a computationally efficient
98   water model under constant pressure and temperature
99   conditions. Crystallization events are interesting in and of
100 < themselves\cite{nucleationStudies}; however, the crystal structure
100 > themselves\cite{Matsumoto02,Yamada02}; however, the crystal structure
101   obtained in this case was different from any previously observed ice
102   polymorphs, in experiment or simulation.\cite{Fennell04} This crystal
103   was termed Ice-{\it i} in homage to its origin in computational
# Line 222 | Line 222 | cubic switching between 100\% and 85\% of the cutoff v
222   \end{figure}
223  
224   Charge, dipole, and Lennard-Jones interactions were modified by a
225 < cubic switching between 100\% and 85\% of the cutoff value (9 \AA ). By
226 < applying this function, these interactions are smoothly truncated,
227 < thereby avoiding poor energy conserving dynamics resulting from
228 < harsher truncation schemes. The effect of a long-range correction was
229 < also investigated on select model systems in a variety of manners. For
230 < the SSD/RF model, a reaction field with a fixed dielectric constant of
231 < 80 was applied in all simulations.\cite{Onsager36} For a series of the
232 < least computationally expensive models (SSD/E, SSD/RF, and TIP3P),
233 < simulations were performed with longer cutoffs of 12 and 15 \AA\ to
234 < compare with the 9 \AA\ cutoff results. Finally, results from the use
235 < of an Ewald summation were estimated for TIP3P and SPC/E by performing
225 > cubic switching between 100\% and 85\% of the cutoff value (9 \AA
226 > ). By applying this function, these interactions are smoothly
227 > truncated, thereby avoiding poor energy conserving dynamics resulting
228 > from harsher truncation schemes. The effect of a long-range correction
229 > was also investigated on select model systems in a variety of
230 > manners. For the SSD/RF model, a reaction field with a fixed
231 > dielectric constant of 80 was applied in all
232 > simulations.\cite{Onsager36} For a series of the least computationally
233 > expensive models (SSD/E, SSD/RF, and TIP3P), simulations were
234 > performed with longer cutoffs of 12 and 15 \AA\ to compare with the 9
235 > \AA\ cutoff results. Finally, results from the use of an Ewald
236 > summation were estimated for TIP3P and SPC/E by performing
237   calculations with Particle-Mesh Ewald (PME) in the TINKER molecular
238 < mechanics software package. TINKER was chosen because it can also
239 < propagate the motion of rigid-bodies, and provides the most direct
240 < comparison to the results from OOPSE. The calculated energy difference
241 < in the presence and absence of PME was applied to the previous results
242 < in order to predict changes in the free energy landscape.
238 > mechanics software package.\cite{Tinker} TINKER was chosen because it
239 > can also propagate the motion of rigid-bodies, and provides the most
240 > direct comparison to the results from OOPSE. The calculated energy
241 > difference in the presence and absence of PME was applied to the
242 > previous results in order to predict changes in the free energy
243 > landscape.
244  
245   \section{Results and discussion}
246  

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