# Lmer model fails to converge

My data is described here What can cause a "Error() model is singular error" in aov when fitting a repeated measures ANOVA?

I am trying to see the effect of an interaction using lmer so my base case is:

my_null.model <- lmer(value ~ Condition+Scenario+
(1|Player)+(1|Trial), data = my, REML=FALSE)

my.model <- lmer(value ~ Condition*Scenario+
(1|Player)+(1|Trial), data = my, REML=FALSE)


Running the anova gives me significant results, but when I try to account for random slope ((1+Scenario|Player)) the model fails with this error:

  Warning messages:
1: In commonArgs(par, fn, control, environment()) :
maxfun < 10 * length(par)^2 is not recommended.
2: In optwrap(optimizer, devfun, getStart(start, rho$lower, rho$pp),  :
convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded
3: In commonArgs(par, fn, control, environment()) :
maxfun < 10 * length(par)^2 is not recommended.
4: In optwrap(optimizer, devfun, opt$par, lower = rho$lower, control = control,  :
convergence code 1 from bobyqa: bobyqa -- maximum number of function evaluations exceeded
5: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
Model failed to converge with max|grad| = 36.9306 (tol = 0.002)
6: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
Model failed to converge: degenerate  Hessian with 1 negative eigenvalues


Alternatively if it fails to converge after a lot of iterations (I set it to 100 000) and I am getting the same results after 50k and 100k it means that it is very close to the actual value, just it does not reach it. So can I report my results like this?

Note that when I set the iterations so high I get only these warnings:

 Warning messages:
1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
Model failed to converge with max|grad| = 43.4951 (tol = 0.002)
2: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,  :
Model failed to converge: degenerate  Hessian with 1 negative eigenvalues


relgrad <- with(fitted_model@optinfo\$derivs,solve(Hessian,gradient))
max(abs(relgrad))