Generative Hierarchical Materials Search
Authors: name: Sherry Yang, name: Simon Batzner, name: Ruiqi Gao, name: Muratahan Aykol, name: Alexander L. Gaunt, name: Brendan McMorrow, name: Danilo J. Rezende, name: Dale Schuurmans, name: Igor Mordatch, name: Ekin D. Cubuk

Generative models trained at scale can now produce text, video, and more
recently, scientific data such as crystal structures. In applications of
generative approaches to materials science, and in particular to crystal
structures, the guidance from the domain expert in the form of high-level
instructions can be essential for an automated system to output candidate
crystals that are viable for downstream research. In this work, we formulate
end-to-end language-to-structure generation as a multi-objective optimization
problem, and propose Generative Hierarchical Materials Search (GenMS) for
controllable generation of crystal structures. GenMS consists of (1) a language
model that takes high-level natural language as input and generates
intermediate textual information about a crystal (e.g., chemical formulae), and
(2) a diffusion model that takes intermediate information as input and
generates low-level continuous value crystal structures. GenMS additionally
uses a graph neural network to predict properties (e.g., formation energy) from
the generated crystal structures. During inference, GenMS leverages all three
components to conduct a forward tree search over the space of possible
structures. Experiments show that GenMS outperforms other alternatives of
directly using language models to generate structures both in satisfying user
request and in generating low-energy structures. We confirm that GenMS is able
to generate common crystal structures such as double perovskites, or spinels,
solely from natural language input, and hence can form the foundation for more
complex structure generation in near future.