MOFormer: Self-Supervised Transformer Model for Metal-Organic Framework Property Prediction.

ABSTRACT: Metal-organic frameworks (MOFs) are materials with a high degree of porosity that can be used for many applications. However, the chemical space of MOFs is enormous due to the large variety of possible combinations of building blocks and topology. Discovering the optimal MOFs for specific applications requires an efficient and accurate search over countless potential candidates. Previous high-throughput screening methods using computational simulations like DFT can be time-consuming. Such methods also require the 3D atomic structures of MOFs, which adds one extra step when evaluating hypothetical MOFs. In this work, we propose a structure-agnostic deep learning method based on the Transformer model, named as MOFormer, for property predictions of MOFs. MOFormer takes a text string represent

SUBMITTER: Cao Z 

PROVIDER: S-EPMC10041520 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature