Default value of learning rate in adam optimizer - Keras
Learning rate is a very important hyperparameter, and often requires some experimentation.  There are some good Related questions here, make sure to check those out.


With too large a learning rate, you might bounce around an optimum, or you might start off by sling-shooting out to a part of the parameter space where the gradients vanish.  (With too small, you might take too long to converge to an optimum, or you might find a poor local optimum.  These effects are lessened by adams momentum effect.)
That said, the best learning rate generally depends on the problem.  Presumably the default was chosen pretty well for general use, but your finding isn't surprising.
There is a parameter decay for simple decay, or you can set up a callback for step decays.