Xie, Zikai, Evangelopoulos, Xenophon, Omar, Ömer H ORCID: 0000-0002-5073-4999, Troisi, Alessandro ORCID: 0000-0002-5447-5648, Cooper, Andrew I ORCID: 0000-0003-0201-1021 and Chen, Linjiang ORCID: 0000-0002-0382-5863
(2024)
Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules.
Chemical science, 15 (2).
pp. 500-510.
Abstract
We evaluate the effectiveness of fine-tuning GPT-3 for the prediction of electronic and functional properties of organic molecules. Our findings show that fine-tuned GPT-3 can successfully identify and distinguish between chemically meaningful patterns, and discern subtle differences among them, exhibiting robust predictive performance for the prediction of molecular properties. We focus on assessing the fine-tuned models' resilience to information loss, resulting from the absence of atoms or chemical groups, and to noise that we introduce <i>via</i> random alterations in atomic identities. We discuss the challenges and limitations inherent to the use of GPT-3 in molecular machine-learning tasks and suggest potential directions for future research and improvements to address these issues.
Item Type: | Article |
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Divisions: | Faculty of Science and Engineering > School of Physical Sciences |
Depositing User: | Symplectic Admin |
Date Deposited: | 30 Jan 2024 10:06 |
Last Modified: | 30 Jan 2024 10:56 |
DOI: | 10.1039/d3sc04610a |
Open Access URL: | https://doi.org/10.1039/D3SC04610A |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3178080 |