3.3. lnη (T ,P ,I )-QSPR model
The lnη (T ,P ,I )-QSPR model was built to
predict η as Eq. (16). The Detailed parameter values are shown in
Table C2 of Supporting Information (atomic-distribution-matrix.docx).
n = 9238, R 2= 0.9100,Q 2 LOCO = 0.8863,Q 2 LOAO = 0.8866;
n training = 7352;
R 2training = 0.9091;
MAEtraining =0.3276 Pas;
n testing = 1886;
R 2testing = 0.9108;
MAEtesting =0.3232 Pas;
where, I IL, I C andI A represent the norm index of ILs, cation and
anion, respectively. n C andn A are the number of cations and anions.
The results of statistical parameters showed that this η model
has high R 2 and low MAE, which demonstrated the
advanced accuracy of the model in predicting η . The experimental
and calculated η values from the model expressed in Eq. (16) are
shown in Table S2 of Supporting Information (exp-cal-values.xlsx).
3.4.1. Model validation
Internal validation. The distribution of cations and
anions as shown in Figures 5a-b. The distribution of cations in theη dataset is more varied and more even than anions. The common
anion, [N(SO2CF3)2], accounts for 34.49% of the η dataset,
and it is worth noting that 255 different cations are combined with
[N(SO2CF3)2]. So, the MAE (0.2034 Pas) of LOAO-CV is relatively
lower for [N(SO2CF3)2]. In addition, it can be seen that all five
cations with a relatively large data points have a low MAE. Figures 6a-b
show the experimental versus calculated values scatter plots of LOCO-CV
and LOAO-CV. Obviously, the satisfactory results of theQ 2LOCO (0.8863) andQ 2LOAO (0.8866) indicate that
the η model has high stability and good prediction performance
for ILs containing new cations and anions. The detailed results for
internal validation of the η model are given in Table 5. It is
clear that the Q 2 for the LOO method
(Q 2LOILO = 0.8908 andQ 2LODPO = 0.9076) are much
higher than the Q 2 for LOIO
(Q 2LOCO = 0.8863 andQ 2LOAO = 0.8866), and the MAE
for the LOO method (MAELOILO = 0.3558 Pas and
MAELODPO = 0.3300 Pas) are lower than the MAE for LOIO
(MAELOCO = 0.3654 Pas and MAELOAO =
0.3677 Pas). This in turn suggests that the stability of the model
validated by LOO-CV is limited to predicting ILs with known ionic types,
while the model passed by the LOIO is more stable when faced with ILs
containing unknown ionic types. In addition, the AE distributions of the
lnη (T ,P ,I )-QSPR model, LOCO-CV and LOAO-CV
are shown in Figure 6d. Here, the AE ranges for the both internal
validations are roughly the same as the
lnη (T ,P ,I )-QSPR model, and the errors of
most data points are concentrated in 0 ~ 0.2 Pas,
further demonstrating the outstanding stability of the
lnη (T ,P ,I )-QSPR model.