Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules-Reference-Cited by-同舟云学术

Fine-tuning GPT-3 for machine learning electronic and functional properties of organic molecules

Published:2024 Issue:2 Volume:15 Page:500-510
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Xie Zikai¹,Evangelopoulos Xenophon¹,Omar Ömer H.²^ORCID,Troisi Alessandro²^ORCID,Cooper Andrew I.¹^ORCID,Chen Linjiang³^ORCID

Affiliation:

1. Leverhulme Research Centre for Functional Materials Design, Materials Innovation Factory and Department of Chemistry, University of Liverpool, Liverpool L7 3NY, UK

2. Department of Chemistry, University of Liverpool, Liverpool L69 3BX, UK

3. School of Chemistry, School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK

Abstract

Fine-tuned GPT-3 shows robust performance for the prediction of electronic and functional properties for organic molecules, with resilience to information loss and noise.

Funder

Leverhulme Trust

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D3SC04610A

Reference22 articles.

1. Machine learning for molecular and materials science

2. RegioML: predicting the regioselectivity of electrophilic aromatic substitution reactions using machine learning

3. Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels