I am fitting a mixed-effects model with the following specification:

    lmer(log(Y) ~ A + B + C + D + (1 + D|M), 
         data = da
I am fitting a mixed-effects model with the following specification:

    mixed_eff_model = lmer(log(Y) ~ A + B + C + D + (1 + D|M), 
                           data = data_df,
                           REML = FALSE,
                           control = lmerControl(optimizer = "Nelder_Mead"))

The output is as follows:

    Linear mixed model fit by maximum likelihood  ['lmerMod']
    Formula: log(Y) ~ A + B +  C + D + (1 + D | M)
    Data: data_df
    Control: lmerControl(optimizer = "Nelder_Mead")

      AIC       BIC    logLik  deviance  df.resid 
     392207.9  392298.8 -196095.0  392189.9    178993 

    Scaled residuals: 
        Min      1Q  Median      3Q     Max 
    -1.4909 -0.6930 -0.2866  0.3907  8.1989 

    Random effects:
     Groups         Name                      Variance Std.Dev. Corr
     M              (Intercept)               2.0710   1.4391       
                     D1                       5.1665   2.2730   0.46
     Residual                                 0.5235   0.7235       
     Number of obs: 179002, groups:  M, 3

    Fixed effects:
                                Estimate Std. Error t value
    (Intercept)               -1.807e+02  3.262e+00 -55.413
    A                         2.462e-03  8.726e-04   2.822
    B                         9.098e-03  5.228e-04  17.402
    C                         9.437e-02  1.563e-03  60.371
    D1                       -1.065e+00  1.312e+00  -0.812


When I plot the residuals using: `plot(mixed_eff_model, type = c('p', 'smooth')`, the output is as follows:

 
[![residual_plot][1]][1]

I have two questions:
1. Does this indicate that the linear model is potentially misspecified?
2. How do I correct the model to account for this observation?

  [1]: https://i.sstatic.net/ovOju.jpg