@inproceedings{
yang2024generative,
title={Generative Hierarchical Materials Search},
author={Sherry Yang and Simon Batzner and Ruiqi Gao and Muratahan Aykol and Alexander L Gaunt and Brendan McMorrow and Danilo Jimenez Rezende and Dale Schuurmans and Igor Mordatch and Ekin Dogus Cubuk},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=PsPR4NOiRC}
}
Generative models trained at scale can now produce novel text, video, and more recently, scientific data such as crystal structures. The ultimate goal for materials discovery, however, goes beyond generation: we desire a fully automated system that proposes, generates, and verifies crystal structures given a high-level user instruction. 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 both in satisfying user request and in generating low-energy structures. GenMS is able to generate complex structures such as double perovskites (or elpasolites), layered structures, and spinels, solely from natural language input.
Generative Hierarchical Materials Search
Sherry Yang Simon Batzner Ruiqi Gao Muratahan Aykol Alexander L Gaunt Brendan McMorrow Danilo Jimenez Rezende Dale Schuurmans Igor Mordatch Ekin Dogus Cubuk