  Force fields developed with machine learning methods in tandem with quantum
mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy,
and (iii) versatility. Recently, we proposed one such approach, wherein, the
vectorial force on an atom is computed directly from its environment. Here, we
discuss the multi-step workflow required for their construction, which begins
with generating diverse reference atomic environments and force data, choosing
a numerical representation for the atomic environments, down selecting a
representative training set, and lastly the learning method itself, for the
case of Al. The constructed force field is then validated by simulating complex
materials phenomena such as surface melting and stress-strain behavior - that
truly go beyond the realm of $ab\ initio$ methods both in length and time
scales. To make such force fields truly versatile an attempt to estimate the
uncertainty in force predictions is put forth, allowing one to identify areas
of poor performance and paving the way for their continual improvement.
