To simulate a molecule you need the forces on every atom. The gold standard, quantum mechanics, is ‘computationally demanding … making long simulations of large systems unfeasible.’ The workaround is a neural network that learns the potential-energy surface itself. Behler & Parrinello introduced one ‘several orders of magnitude faster than DFT’; ANI-1 delivered ‘DFT accuracy at force field computational cost,’ transferable across organic chemical space; SchNet showed deep nets are ‘ideally suitable for representing quantum-mechanical interactions.’ DeePMD made it scale — quantum-accurate molecular dynamics ‘at a cost that scales linearly with system size’ — and NequIP baked in the symmetries of 3D space, ‘challenging the widely held belief that deep neural networks require massive training sets’ by learning from ‘up to three orders of magnitude fewer training data.’ As Unke et al. put it, the aim is ’to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs.’ It’s the physics engine a virtual cell will run on.
