Work

Large Language Models for Inorganic Synthesis Predictions

Year: 2024

Type: article

Abstract: We evaluate the effectiveness of pretrained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis. The predictions of fine-tuned LLMs are comparable to─and sometimes better than─recent bespoke machine learning models for these tasks but require only minimal user expertise, cost, and time to develop. Therefore, this strategy can serve both as an effective and strong baseline for future machine learning studies of various chemical applications and as a practical tool for experimental chemists.

Source: Journal of the American Chemical Society

DOI: 10.1021/jacs.4c05840

Authors: Seongmin Kim, Yousung Jung, Joshua Schrier

Institutions: Korea Advanced Institute of Science and Technology, Seoul National University, Institute of Engineering Research, Fordham University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green