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Large language model for materials science

Published on:

11 October 2023

Primary Category:

Materials Science

Paper Authors:

Ziyi Chen,

Fankai Xie,

Meng Wan,

Yang Yuan,

Miao Liu,

Zongguo Wang,

Sheng Meng,

Yangang Wang

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Key Details

MatChat model fine-tuned from LLaMA2-7B using materials synthesis data

13,878 synthesis processes extracted from papers used for training

MatChat shows strong reasoning abilities for material synthesis

Both generative and inferential capabilities demonstrated

Web platform developed for easy access to MatChat model

AI generated summary

Large language model for materials science

This paper trains a large language model called MatChat to predict inorganic material chemical synthesis pathways. The authors fine-tune the open-source LLaMA2-7B model using a dataset of 13,878 synthesis processes extracted from papers. Experiments show MatChat can reason about and generate high-quality synthesis methods not seen during training.

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