My PhD was based on molecular dynamics simulations of novel battery anodes. I remember thinking back in 2016 that machine learning would probably be used to cre

My PhD was based on molecular dynamics simulations of novel battery anodes. I remember thinking back in 2016 that machine learning would probably be used to create much more effective MD potentials than the ad-hoc, rule-based force fields in use at the time, with ReaxFF being state-of-the-art for certain types of potentials that account for chemical reactions. My thinking was that you would just train some symmetry-invariant NNs on various molecular environments that had chemical properties computed using something higher quality like DFT or other QC approaches. A brief literature search seems to indicate this hasn't really happened for some reason — does anyone in the field know why? I know ReaxFF computations were dead simple to perform at massive scale in LAMMPS, perhaps NNs are more difficult to scale up for longer-range potentials?