I’m really excited to read the paper for this! I’ve been pondering for a while now how well DNN’s would model complex QC wave functions. Much high level quantum chemistry research involves human theoreticians finding “quirks” and other features which allow computing specific properties like say estimating quantum tunneling in photosynthesis. These involve high level symmetries or various green’s functions which help correlate to different domains. But most simulation only use DFT (roughly discretized orbitals) or other “naive” optimization methods over a pure numerical solution of the ache of infer equations.<p>Still running true ab initio QM simulations of a few atoms can take months on a single computer (never had a chance to run simulations on a cluster or GPU). DNN’s however have the ability to find higher dimensional patterns difficult for humans to find but which could significantly speed up QM simulations.<p>Currently doing QM simulations of chemical reactions for any number of reactions is in feasible but if work like FermiNet could make it feasible for small teams to simulate more complex chemical reactions it could open up an entire field of chemical/industrial processes to startups. As in you could reasonably simulate chemical processes sufficiently to optimize current process or find entirely novel reactions. This would significantly reduce the capital expenses most research in these areas require.<p>In short if I were a VC I would be _very_ keen I’m watching this field. There tremendous value hidden behind this general problem.