  Particle track reconstruction is the most computationally intensive process
in nuclear physics experiments. Traditional algorithms use a combinatorial
approach that exhaustively tests track measurements ("hits") to identify those
that form an actual particle trajectory. In this article, we describe the
development of four machine learning (ML) models that assist the tracking
algorithm by identifying valid track candidates from the measurements in drift
chambers. Several types of machine learning models were tested, including:
Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely
Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this
work, an MLP network classifier was implemented as part of the CLAS12
reconstruction software to provide the tracking code with recommended track
candidates. The resulting software achieved accuracy of greater than 99\% and
resulted in an end-to-end speedup of 35\% compared to existing algorithms.
