Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Large language models, commonly known as LLMs, are showing promise in tacking some of the most complex tasks in AI. In this perspective, we review the wider field of foundation models—of which LLMs are a component—and their application to the field of materials discovery. In addition to the current state of the art—including applications to property prediction, synthesis planning and molecular generation—we also take a look to the future, and posit how new methods of data capture, and indeed modalities of data, will influence the direction of this emerging field.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Amarachi Blessing Mbakwe, Joy Wu, et al.
NeurIPS 2023
Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023
Baihan Lin, Guillermo Cecchi, et al.
IJCAI 2023