In this section
@article{Lei:2024:10.1039/d4dd00074a,
author = {Lei, G and Docherty, R and Cooper, SJ},
doi = {10.1039/d4dd00074a},
journal = {Digital Discovery},
pages = {1257--1272},
title = {Materials science in the era of large language models: a perspective},
url = {http://dx.doi.org/10.1039/d4dd00074a},
volume = {3},
year = {2024}
}
TY - JOUR
AB - Large Language Models (LLMs) have garnered considerable interest due to their impressive natural language capabilities, which in conjunction with various emergent properties make them versatile tools in workflows ranging from complex code generation to heuristic finding for combinatorial problems. In this paper we offer a perspective on their applicability to materials science research, arguing their ability to handle ambiguous requirements across a range of tasks and disciplines means they could be a powerful tool to aid researchers. We qualitatively examine basic LLM theory, connecting it to relevant properties and techniques in the literature before providing two case studies that demonstrate their use in task automation and knowledge extraction at-scale. At their current stage of development, we argue LLMs should be viewed less as oracles of novel insight, and more as tireless workers that can accelerate and unify exploration across domains. It is our hope that this paper can familiarise materials science researchers with the concepts needed to leverage these tools in their own research.
AU - Lei,G
AU - Docherty,R
AU - Cooper,SJ
DO - 10.1039/d4dd00074a
EP - 1272
PY - 2024///
SN - 2635-098X
SP - 1257
TI - Materials science in the era of large language models: a perspective
T2 - Digital Discovery
UR - http://dx.doi.org/10.1039/d4dd00074a
UR - http://hdl.handle.net/10044/1/112447
VL - 3
ER -
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