Machine learning potentials for extended systems: a perspective

Abstract In the past two and a half decades machine learning potentials have evolved from special purpose solution to broadly applicable tool for large-scale atomistic simulations. By combining efficiency of empirical force fields with an accuracy close first-principles calculations they now enable computer simulations wide range molecules materials. this perspective, we summarize present status these new types models extended systems, which are increasingly used materials modelling. There several approaches, but all in common that exploit locality atomic properties some form. Long-range interactions, most prominently electrostatic can also be included even systems non-local charge transfer leads electronic structure depends globally on positions. Remaining challenges limitations current approaches discussed. Graphic