Online Fritz-Haber Seminar with Prof. Alexandre Tkatchenko from the University of Luxembourg: "On Electrons and Machine Learning Force Fields." 

Abstract
Machine Learning Force Fields (MLFF) should be accurate, efficient, and applicable to
molecules, materials, and interfaces thereof. The first step toward ensuring broad
applicability and reliability of MLFFs requires a robust conceptual understanding of how to
map interacting electrons to interacting "atoms". Here I discuss two aspects: (1) how
electronic interactions are mapped to atoms with a critique of the "electronic

nearsightedness" principle, and (2) our developments of symmetry-adapted gradient-
domain machine learning (sGDML) framework for MLFFs generally applicable for modeling

of molecules, materials, and their interfaces. I highlight the key importance of bridging
fundamental physical priors and conservation laws with the flexibility of non-linear ML
regressors to achieve the challenging goal of constructing chemically-accurate force fields
for a broad set of systems. Applications of sGDML will be presented for small and large
(bio/DNA) molecules, pristine and realistic solids, and interfaces between molecules and
2D materials.
References
Sci. Adv. 3, e1603015 (2017); Nat. Commun. 9, 3887 (2018); Comp. Phys. Comm.
240, 38 (2019); J. Chem. Phys. 150, 114102 (2019); Sci. Adv. 5, eaax0024 (2019).