Machine Learning Force Fields: Construction, Validation, and Outlook

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 multistep 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.