  We present an optimized implementation of the recently proposed symmetric
gradient domain machine learning (sGDML) model. The sGDML model is able to
faithfully reproduce global potential energy surfaces (PES) for molecules with
a few dozen atoms from a limited number of user-provided reference molecular
conformations and the associated atomic forces. Here, we introduce a Python
software package to reconstruct and evaluate custom sGDML force fields (FFs),
without requiring in-depth knowledge about the details of the model. A
user-friendly command-line interface offers assistance through the complete
process of model creation, in an effort to make this novel machine learning
approach accessible to broad practitioners. Our paper serves as a
documentation, but also includes a practical application example of how to
reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to
interface sGDML with the FF simulation engines ASE (Larsen et al., J. Phys.
Condens. Matter 29, 273002 (2017)) and i-PI (Kapil et al., Comput. Phys.
Commun. 236, 214-223 (2019)) to run numerical experiments, including structure
optimization, classical and path integral molecular dynamics and nudged elastic
band calculations.
