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.
66e25b1a5b1fc436d9ffeb73
Sherry Yang
66e25b1a5b1fc436d9ffeb74
Simon Batzner
66e25b1a5b1fc436d9ffeb75
Ruiqi Gao
admin_assigned
2024-09-12T10:31:31.303Z
66a1596f614dd62973602839
/avatars/aacef148ec79ec55d6518fcab5b3fedf.svg
Ruiqi Gao
user
RickWork
66e25b1a5b1fc436d9ffeb76
Muratahan Aykol
66e25b1a5b1fc436d9ffeb77
Alexander L. Gaunt
66e25b1a5b1fc436d9ffeb78
Brendan McMorrow
66e25b1a5b1fc436d9ffeb79
Danilo J. Rezende
66e25b1a5b1fc436d9ffeb7a
Dale Schuurmans
66e25b1a5b1fc436d9ffeb7b
Igor Mordatch
admin_assigned
2024-09-12T10:33:09.010Z
6371023d789970f7bc5eecd6
/avatars/6b04b345ddd6add886a1f14cf995a8e6.svg
Igor M
user
IgorM
66e25b1a5b1fc436d9ffeb7c
Ekin D. Cubuk
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.
GenMS combines a language model, diffusion model, and graph neural network to generate crystal structures from natural language, improving satisfaction of user requests and energy efficiency.
language-to-structure generation multi-objective optimization Generative Hierarchical Materials Search language model diffusion model graph neural network forward tree search crystal structures formation energy