sGDML: Constructing accurate and data efficient molecular force fields using machine learning. - Stefan Chmiela; Huziel Enoc Sauceda Felix; Igor Poltavsky; Klaus-Robert Müller; Alexandre Tkatchenko (Comput. Phys. Commun., 2019)

i-PI 2.0: A universal force engine for advanced molecular simulations. - Venkat Kapil; Mariana Rossi; Ondrej Marsalek; Riccardo Petraglia; Yair Litman; Thomas Spura; Bingqing Cheng; Alice Cuzzocrea; Robert H. Meißner; David M. Wilkins; Benjamin A. Helfrecht; Przemyslaw Juda; et al. (Comput. Phys. Commun., 2019)

A practical guide to machine learning interatomic potentials - Status and future. - Ryan Jacobs; Dane Morgan; Siamak Attarian; Jun Meng; Chen Shen; Zhenghao Wu; Clare Yijia Xie; Julia H. Yang; Nongnuch Artrith; Ben Blaiszik; Gerbrand Ceder; Kamal Choudhary; et al. (CoRR, 2025)