Chapter 3 Mixing and validation of OPLS-AA and TIP4P/Ice force-
3.2 Methods
3.2.2 Simulation parameters
Pure liquid water simulations
The forcefield’s compatibility with various water models were validated by simu-
lating a pure water system at ambient (298.15 K and 1 bar) and periodic boundary conditions. For these simulations 4,008 water molecules are placed in a cubic simula- tion box of approx. 4.95 nm3. The simulations are performed in the NPT ensemble
using the GROMACS simulation package[232,233] and the OPLS-AA forcefield. In
all simulations the Leapfrog algorithm was was used to solve the equations of mo- tion, and the SETTLE[213]algorithm was applied to constrain the bond length and angles of the water models in order to allow a larger integration time step of 2 fs. Lennard-Jones (LJ) potentials were cut-o↵at 0.85 nm and the particle-mesh Ewald (PME) algorithm[197,198] was employed which meant that electrostatics could be treated accurately. The real part of the Coulombic potential was also truncated at 0.85 nm while the width of the mesh was 0.16 nm and a cubic order polynomial was used. The Nos´e-Hoover thermostat[209,234] was used alongside the Parinello- Rahman Barostat[210] to maintain isothermic-isobaric conditions. The relaxation times for the thermostat and barostat were 0.2 ps and 2 ps respectively. Isotropic conditions were maintained in this simulation, using the compressibility of water at 4.5⇥10−5bar−1. Unless stated otherwise, the temperature and cut-o↵s were main-
tained at 298.15 K and 0.85 nm respectively.
Each simulation lasted 3 ns and the first 1 ns of each run was discarded as
equilibration. The remaining 2 ns was used for analysis. This range was selected by monitoring the conversion of the O-O RDFs of water over time (typically achieved by 0.9 ns, using short consecutive time blocks of 0.3 ns e.g. t0=0–0.3 ns, t1=0.3-0.6 ns ... t10=2.7-3.0 ns).
Saline solution simulations
In order to validate the compatibility of the forcefields with small molecules like salts, and to compare the the simulations with the pure water systems, similar con- ditions as above were employed. The only exception was that water molecules were systematically and randomly replaced with Na and Cl ions, in order to achieve the specified concentration outlined in Table 3.2.
Conc. (M) 0.10 0.14 0.21 0.28 0.35 0.43
Water 3,994 3,988 3,978 3,968 3,956 3,946
Na 7 10 15 20 26 31
Cl 7 10 15 20 26 31
Table 3.2: The number of water molecules and ions in the saline solutions. Overall, the systems still remain neutral.
Water/ethanol mixtures
A single ethanol molecule was energy minimised using the steepest decent algorithm for 20,000 steps. Then a box of water was made and solvated with multiple ethanol molecules as specified in Table 3.3. To avoid unrealistic initial forces in the sim- ulation, the system was energy minimized two more times under the same prior conditions before the final production run (once with flexible bonds on the ethanol and once with constrained bonds). During this 3 ns simulation, all of ethanol’s hy- drogen bonds were enforced using LINCS algorithm[212]. Although we use twice as many molecules in the system, the following protocol serves as comparison to the results from Wensinket al[12].
M 10 20 30 40 50 60 70 80 90 100
Wat 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0
Eth 78 156 234 312 392 470 548 616 704 782
X 0.042 0.088 0.143 0.206 0.281 0.370 0.477 0.610 0.779 1.000
Table 3.3: The percentage mass (M) and mole (X) fraction of ethanol (Eth) in the
solution and the number of water (Wat) and ethanol molecules in the mixtures.
Free Energy of Solvation of Ethanol
A single energy minimised ethanol molecule was arranged in a cubic box approx-
imately 2.4 nm3 and was solvated by 310 water molecules. The system was energy
minimized again and equilibrated for 3 ns at the same conditions employed for the ethanol-water mixtures. Finally the production run was conducted using the leap- frog stochastic dynamics integrator, using lambda values from 0 to 1 with 0.1 inter-
vals. Thefirst 1 ns of the simulation was discarded as equilibration and the last 2 ns
were evaluated using the Bennett Acceptance Ratio[235] (BAR) method.
The BAR is an algorithm which is used to estimate the free energy di↵erence between two di↵erent states (AandB) and so it requires configurational data from both states. The BAR works under the principal there is a pathway connecting the states and likewise for the energy associated with them. Assuming that Aand
B have common configurational space, the free energy di↵erence between the two states, ∆G, can be calculated by sampling both ensembles (Eq. 3.1).
∆G=GB−GA (3.1)
In many cases the two end states have little overlap in phase space, so we define a series of intermediates using a coupling parameter or variable known as lambda (λ). Lambda describes the non-physical transformation from A (λ = 0) to B (λ = 1) at user-defined intervals e.g. δλ = 0.1. The sum of the potential energies (U) associated these intermediate states is equivalent to the overall free energy change during the transition from the initial state, A, to the end state, B. A schematic of the free energy of solvation is shown in Fig. 3.1, where interactions between the water and the ethanol molecule is linearly and gradually de-coupled.
Figure 3.1: A schematic of the free energy of solvation. The ethanol molecule is represented by the green ball and the solvent is shown in blue.
Bennett showed that the free energy di↵erence of a substep (∆G(λl!λl+1))
can be described by Eq. 3.2 provided that the unknown constant, C, can be found
iteratively based on the condition in Eq. 3.3.
∆G(λl!λl+1) =−kBT lnnλl+1 nλl +C (3.2) X 1f(Uλl−Uλl+1+C) = X 0f(Uλl+1−Uλl−C) (3.3)
wheref is the Fermi function,nλl is the coordinate frames for the initial state
used to calculate the free energy di↵erence between substeps, andnλl+1 represents
the coordinates of thefinal state.
Since the free energy of a system is a state variable, only the end states mat- ter and so the free energy di↵erence can be calculated in this manner. It is worth noting that the Helmholtz free energy di↵erence is determined in NVT conditions, while the Gibbs free energy is obtained in NPT conditions such as our own. Other algorithms can be used to calculate the free energy such as Exponential Averag-
ing[236] (EXP) and Thermodynamic Intergration[237] (TI) methods, however these
methods use an ensemble average from a single state. As a result these methods are are significantly more biased than the BAR method.[238]
Freezing and melting simulations
These simulations are used to determine the melting and freezing kinetics of the TIP4P/Ice water model. It is used to check that this particular model still main- tains its good representation of the water phase diagram in conjunction with the OPLS-AA forcefield. For these simulations we followed a similar protocol to that of
Weisset al[19] and Fern´andezet al[219].
Essentially the simulation parameters are identical to the liquid simula-
tion unless stated otherwise. A 2.9⇥3.1⇥2.7 nm box of hexagonal ice (768 water
molecules) is equilibrated in an NVT simulation at 10 K. The simulation lasts 100 ps, using the Nos´e-Hoover thermostat to maintain the temperature. The reason for this is that any bad initial contacts are given the chance to resolve themselves, while the bulk of the ice crystal remains frozen at sub-freezing temperatures. In compar- ison to a single interface, this set up allows a more realistic environment where the
ice seed is surrounded by significant amounts of water. Next the equilibrated ice
is solvated so that it is sandwiched between two water layers of similar size (763 molecules each). It is oriented in such a way that the fastest growing ice plane (the 11¯20 face or the secondary prism plane[219]) is exposed to the liquid water, creat- ing an interface at either side. This initial configuration is known as a two-phase coexistence or the direct coexistence method because both sides of the ice crystal are in direct contact with the liquid water[219]. This approach is important as the nucleation of ice is an activated process[219,239]and often requires a seed, so merely simulating pure liquid water below the freezing temperature usually produces super- cooled water instead of ice. This has only once been successfully achieved following a one year NVT simulation by Matsumotoet al[240]. Additionally the presence of this interface also removes the phenomena of bulk solid superheating, which is a erroneous computational occurrence and an experimental impossibility.[219]
The new system contains 2,294 molecules and a second NVT run with 10,000 kJ mol−1 position restraints placed on the oxygen atoms that belong to the ice. These restraints mean that the atoms are only allowed to change their posi- tions, provided that the atoms have the specified amount of energy, or more. This time the temperature is maintained at 283.15 K. It allows the water to equilibrate around the ice and the same conditions are conducted for a short consecutive NPT run using 10,000 kJ mol−1 and 100 kJ mol−1respectively.
Once the NPT equilibration runs are complete, the restraints are removed and NPT production runs are conducted at di↵erent temperatures until melting or freezing is observed. In these simulations a 1 fs timestep was employed in order to accurately capture the shorter time scales reported for melting at high temperatures. During the NPT (equilibration and production) simulations, the box is allowed to fluctuate independently in all directions. This allows the system to freely freeze or melt, and the box shape can adjust accordingly. To accomplish this in GROMACS with Parinello-Rahman barostat, the compressibilities are specified and issued in such a way that it helps to scale the strength (or speed) of the pressure coupling and
the box deformation. The compressibility of thex- andy-planes are both set to the
compressibility of real ice (1.0⇥10−5bar−1), while thez-direction is perpendicular to
the ice/water interface so it remains set to the higher compressibility of real water. The angles are kept orthogonal and unlike the original Weiss simulations, the LJ potential was truncated using 0.85 nm cut-o↵s. To compare the e↵ects of cut-o↵s, this same protocol was also repeated for the 1 nm cut-o↵.
Prior to evaluating the results of the simulation, it was necessary to demon- strate that the system had reached equilibrium. This process is monitored by the
convergence of the total energy. The melting process is identified by an increase in
the total energy and freezing by a decrease in the total energy. Once this process is complete, a plateau is observed. Additionally, just before the plateau a characteris- tic spike to the baseline serves as an initial indicator that a few of the interface layers remain to be processed and so melting or freezing is especially fast. Similar to Weiss
et al[19], we acknowledge that the direct monitoring of the potential energy would
also have been sufficient to monitor the melting and freezing of the system, however in order to allow direct comparisons we decided to follow the approach reported by Fern´andez et al[219].