Crystal Composition Transformer: Self-Learning Neural Language Model for Generative and Tinkering Design of Materials.

ABSTRACT: Self-supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking-based pre-trained language models are not designed for generative design, and their black-box nature makes it difficult to interpret their design logic. Here a Blank-filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network-based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank-filling lan

SUBMITTER: Wei L 

PROVIDER: S-EPMC11423232 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature