Investigating the Behavior of Diffusion Models for Accelerating
  Electronic Structure Calculations
Daniel Rothchild Andrew S. Rosen Eric Taw Connie Robinson Joseph E. Gonzalez Aditi S. Krishnapriyan
We present an investigation into diffusion models for molecular generation,
with the aim of better understanding how their predictions compare to the
results of physics-based calculations. The investigation into these models is
driven by their potential to significantly accelerate electronic structure
calculations using machine learning, without requiring expensive
first-principles datasets for training interatomic potentials. We find that the
inference process of a popular diffusion model for de novo molecular generation
is divided into an exploration phase, where the model chooses the atomic
species, and a relaxation phase, where it adjusts the atomic coordinates to
find a low-energy geometry. As training proceeds, we show that the model
initially learns about the first-order structure of the potential energy
surface, and then later learns about higher-order structure. We also find that
the relaxation phase of the diffusion model can be re-purposed to sample the
Boltzmann distribution over conformations and to carry out structure
relaxations. For structure relaxations, the model finds geometries with ~10x
lower energy than those produced by a classical force field for small organic
molecules. Initializing a density functional theory (DFT) relaxation at the
diffusion-produced structures yields a >2x speedup to the DFT relaxation when
compared to initializing at structures relaxed with a classical force field.