# Predicting Melting Points of Biofriendly Choline-Based Ionic Liquids with Molecular Dynamics
## 3 Results
### Predicted Melting Points  
For each IL, the solid phases were packed and the systems determining the melting points were identified to the criteria defined in the methods section. Following this, solid and liquid phase linear fits were made. For example, Figure 3 displays the \(D\)-\(T\) dependence of choline ibuprofenatic solid phase during annealing along with the fitted lines--red for solid phase and green for liquid phase. Similar plots for all of the studied ILs are provided in the Supplementary Materials. It is evident from Figure 3 that although the liquid and solid phases' fit is quite strong, the phase transition from solid to liquid introduces a slightly curved region. A more severe example of non-linearity among the simulated systems is choline saccharinate (Figure 4). This phenomenon occurs most likely due to the presence of more than one competing diffusion mechanism. In this case, the \(D\)-\(T\) dependence cannot be properly linearized using the Arrhenius' equation \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Cation: Choline** & \(T_{\rm G}\) & \(T_{\rm M}\) & **T Range** & **Water Content** \\
**Anion** & **(K)** & **(K)** & **(K)** & **(\%)** \\ \hline Acetate & 208 & 354 & 173–373 & 2.66 \\ Citrate & n/d & 378 & 323–423 & \textless{}0.01 \\ Glutamate & 215 & n/d & 173–373 & 0.02 \\ Isobutanoate & 207 & 341 & 173–373 & 0.14 \\ Lactate & 211 & n/d & 173–373 & 0.77 \\ Isovalerate & n/d & 334 & 323–423 & 0.02 \\ Malonate & 206 & n/d & 173–373 & 0.24 \\
2-Methylbutanoate & 212 & 363 & 173–373 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The experimental glass transition temperatures \(T_{\rm G}\) and melting points \(T_{\rm M}\) of choline cation-based ionic liquids measured with differential scanning calorimetry (DSC). \(T\) range indicates the scanned temperature range. Water content was measured with Karl Fischer titration. Figure 3: The diffusion coefficient (\(D\)) dependence on temperature (\(T\)) during annealing simulation of choline ibuprofenatic. The solid phase was packed at 1.2 kg/dm\({}^{3}\) density with potential wells in CsCl lattice with 4:3:2 vector ratio. Red, green and the dotted line indicate respectively the fitted solid phase region, the fitted liquid phase region and the experimental melting point. (Equation (1)), as different mechanisms may have different activation energies \(E_{\mathrm{A}}\) and pre-exponent factors \(D_{0}\). Regardless of whether this situation arises in pure phases, it is unavoidable at phase transfer conditions where different phases and/or their transition states coexist. Given an infinitely long simulation, the melting process goes to completion at a discrete temperature provided that the system is crystalline. However, at simulation size and time scales, melting occurs over a range of temperatures. The diffusion mechanism is different and complex during the time when the system has begun to melt, but its structure is still dependent on the initial solid phase. This effect is manifested as non-linearity in the \(D\)-\(T\) dependence and it lowers the accuracy of the melting point prediction. Despite the described non-linearity, the prediction of melting points is possible through an assumption, that melting point is the temperature at which the fitted solid phase dependence ends. The system at the end of the solid fit may be a complex transitional phase instead of a proper solid or liquid phase, but the end-point can still be mathematically determined. To increase robustness, we excluded from analysis systems with correlation coefficients below 0.7 and 0.9 for the solid and liquid phase fits, respectively. This way we avoided systems with large non-linearity and oscillations at phase transition. For choline saccharinate, a 0.7 minimum liquid phase fit was allowed, as none of its systems produced a liquid phase fit of above 0.9. Note that simulations with different partial charges or temperature rates have weaker correlations requiring lower \(R^{2}\) thresholds.