Generative Hierarchical Materials Search
<|reference_start|>Generative Hierarchical Materials Search: 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.<|reference_end|>
@article{yang2024generative,
    title={Generative Hierarchical Materials Search},
    author={Sherry Yang, Simon Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L.
  Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch,
  and Ekin D. Cubuk},
    journal={arXiv preprint arXiv:2409.06762},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.06762},
    primaryClass={cond-mat.mtrl-sci cs.AI}
}