  In computational chemistry, crystal structure prediction (CSP) is an
optimization problem that involves discovering the lowest energy stable crystal
structure for a given chemical formula. This problem is challenging as it
requires discovering globally optimal designs with the lowest energies on
complex manifolds. One approach to tackle this problem involves building
simulators based on density functional theory (DFT) followed by running search
in simulation, but these simulators are painfully slow. In this paper, we study
present and study an alternate, data-driven approach to crystal structure
prediction: instead of directly searching for the most stable structures in
simulation, we train a surrogate model of the crystal formation energy from a
database of existing crystal structures, and then optimize this model with
respect to the parameters of the crystal structure. This surrogate model is
trained to be conservative so as to prevent exploitation of its errors by the
optimizer. To handle optimization in the non-Euclidean space of crystal
structures, we first utilize a state-of-the-art graph diffusion auto-encoder
(CD-VAE) to convert a crystal structure into a vector-based search space and
then optimize a conservative surrogate model of the crystal energy, trained on
top of this vector representation. We show that our approach, dubbed LCOMs
(latent conservative objective models), performs comparably to the best current
approaches in terms of success rate of structure prediction, while also
drastically reducing computational cost.
