title: Generative Hierarchical Materials Search
summary: 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.
paper_authors._id: 66e25b1a5b1fc436d9ffeb73
paper_authors.name: Sherry Yang
paper_authors._id: 66e25b1a5b1fc436d9ffeb74
paper_authors.name: Simon Batzner
paper_authors._id: 66e25b1a5b1fc436d9ffeb75
paper_authors.name: Ruiqi Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-12T10:31:31.303Z
paper_authors._id: 66a1596f614dd62973602839
paper_authors.avatarUrl: /avatars/aacef148ec79ec55d6518fcab5b3fedf.svg
paper_authors.fullname: Ruiqi Gao
paper_authors.type: user
paper_authors.user: RickWork
paper_authors._id: 66e25b1a5b1fc436d9ffeb76
paper_authors.name: Muratahan Aykol
paper_authors._id: 66e25b1a5b1fc436d9ffeb77
paper_authors.name: Alexander L. Gaunt
paper_authors._id: 66e25b1a5b1fc436d9ffeb78
paper_authors.name: Brendan McMorrow
paper_authors._id: 66e25b1a5b1fc436d9ffeb79
paper_authors.name: Danilo J. Rezende
paper_authors._id: 66e25b1a5b1fc436d9ffeb7a
paper_authors.name: Dale Schuurmans
paper_authors._id: 66e25b1a5b1fc436d9ffeb7b
paper_authors.name: Igor Mordatch
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-09-12T10:33:09.010Z
paper_authors._id: 6371023d789970f7bc5eecd6
paper_authors.avatarUrl: /avatars/6b04b345ddd6add886a1f14cf995a8e6.svg
paper_authors.fullname: Igor M
paper_authors.type: user
paper_authors.user: IgorM
paper_authors._id: 66e25b1a5b1fc436d9ffeb7c
paper_authors.name: Ekin D. Cubuk
paper_title: Generative Hierarchical Materials Search
paper_summary: 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.
paper_ai_keywords: Generative Hierarchical Materials Search (GenMS) multi-objective optimization language model diffusion model graph neural network forward tree search