Speaker
Description
Data-driven materials discovery is coming of age, given the rise of 'big data' and machine-learning (ML) methods. However, the most sophisticated ML methods need a lot of data to train them. Such data may be custom materials databases that comprise chemical names and their cognate properties for a given functional application; or data may comprise a large corpus of text to train a language model. This talk showcases our home-grown open-source software tools that have been developed to auto-generate custom materials databases for a given application. The presentation will also demonstrate how domain-specific language models can now be used as interactive engines for data-driven materials science; The talk concludes with a forecast of how this 'paradigm shift' away from the use of static databases will likely evolve next-generation materials science.