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Leveraging large language models for predictive chemistry

Chemistry

Leveraging large language models for predictive chemistry

K. M. Jablonka, P. Schwaller, et al.

This groundbreaking research conducted by Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, and Berend Smit demonstrates how GPT-3 can revolutionize chemistry and materials science tasks, outperforming traditional machine learning in low-data scenarios. The model's capacity for inverse design and ease of use holds transformative potential for these fields.

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Playback language: English
Abstract
This paper explores the application of GPT-3, a large language model, to various tasks in chemistry and materials science. By fine-tuning GPT-3 to answer chemical questions in natural language, the authors demonstrate its comparable or superior performance to conventional machine learning techniques, particularly in low-data scenarios. The model's ability to perform inverse design by inverting questions and its ease of use are highlighted as significant advantages, potentially impacting the fundamental approach to machine learning in these fields.
Publisher
Nature Machine Intelligence
Published On
Feb 06, 2024
Authors
Kevin Maik Jablonka, Philippe Schwaller, Andres Ortega-Guerrero, Berend Smit
Tags
GPT-3
chemistry
materials science
machine learning
inverse design
natural language processing
low-data scenarios

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