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Found 3 papers, 1 papers with code
Materials Transformers Language Models for Generative Materials Design: a benchmark study
We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.
Multitask Learning for Citation Purpose Classification
The goal of the competition is to classify a citation in a scientific article based on its purpose.
Active learning based generative design for the discovery of wide bandgap materials
Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model.
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