Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods

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DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models (2019, methodology): “To this end, the ab initio molecular dynamics (AIMD) scheme, wherein accurate PES and interatomic forces are obtained within the density functional theory (DFT) approximation, has been most widely adopted [1–3].”
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (2017, background): “On one hand, ab initio molecular dynamics (AIMD) has the accuracy of the density functional theory (DFT) [1–3], but the computational cost of DFT in evaluating the PES and forces restricts its typical applications to system size of hundreds to thousands of atoms and time scale of ∼ 100 ps .”
Maximally-localized Wannier Functions: Theory and Applications (2011, background): “…molecular dynamics, with discussions of MLWFs in these contexts, have appeared reviews by Dovesi et al. (2005); Kirchner (2007); Tse (2002); Tuckerman (2002); Tuckerman and Martyna (2000); and Vuilleumier (2006), with Marx and Hutter (2009) providing a very comprehensive methodological overview.”
End-to-end differentiable learning of protein structure (2018, methodology): “This includes physics-based molecular dynamics simulations (Marx and Hutter, 2012), which are restricted by computational cost to small proteins, and fragment assembly methods (Gajda et al.”