Molecular relaxation by reverse diffusion with time step prediction
Khaled Kahouli Stefaan Simon Pierre Hessmann Klaus-Robert Müller Shinichi Nakajima Stefan Gugler Niklas Wolf Andreas Gebauer
Molecular relaxation, finding the equilibrium state of a non-equilibrium
structure, is an essential component of computational chemistry to understand
reactivity. Classical force field methods often rely on insufficient local
energy minimization, while neural network force field models require large
labeled datasets encompassing both equilibrium and non-equilibrium structures.
As a remedy, we propose MoreRed, molecular relaxation by reverse diffusion, a
conceptually novel and purely statistical approach where non-equilibrium
structures are treated as noisy instances of their corresponding equilibrium
states. To enable the denoising of arbitrarily noisy inputs via a generative
diffusion model, we further introduce a novel diffusion time step predictor.
Notably, MoreRed learns a simpler pseudo potential energy surface instead of
the complex physical potential energy surface. It is trained on a significantly
smaller, and thus computationally cheaper, dataset consisting of solely
unlabeled equilibrium structures, avoiding the computation of non-equilibrium
structures altogether. We compare MoreRed to classical force fields,
equivariant neural network force fields trained on a large dataset of
equilibrium and non-equilibrium data, as well as a semi-empirical tight-binding
model. To assess this quantitatively, we evaluate the root-mean-square
deviation between the found equilibrium structures and the reference
equilibrium structures as well as their DFT energies.