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Large Language Models for Inorganic Synthesis Predictions.

Seongmin Kim¹, Yousung Jung^2,3,4,5, Joshua Schrier⁶

¹Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.

Journal of the American Chemical Society

|July 11, 2024

Summary

This summary is machine-generated.

Large language models (LLMs) effectively predict inorganic compound synthesizability and precursor selection. Fine-tuned LLMs offer a practical, cost-effective alternative to complex machine learning models for chemists.

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Area of Science:

Materials Science
Computational Chemistry
Artificial Intelligence

Background:

Predicting the synthesizability of inorganic compounds is crucial for materials discovery.
Developing bespoke machine learning models requires significant expertise, time, and cost.
Large language models (LLMs) present a potential alternative for chemical prediction tasks.

Purpose of the Study:

To evaluate the effectiveness of pretrained and fine-tuned LLMs for predicting inorganic compound synthesizability.
To assess the ability of LLMs to select appropriate precursors for inorganic synthesis.
To establish LLMs as a practical tool and baseline for chemical machine learning.

Main Methods:

Utilizing pretrained and fine-tuned large language models.
Applying models to predict the synthesizability of inorganic compounds.
Using models to identify suitable precursors for chemical synthesis.

Main Results:

Fine-tuned LLMs demonstrate predictive performance comparable to, or exceeding, bespoke machine learning models.
LLM-based predictions require minimal user expertise, cost, and development time.
The models successfully predicted synthesizability and precursor selection for inorganic compounds.

Conclusions:

Fine-tuned LLMs provide an effective and accessible strategy for predicting inorganic synthesizability and precursor selection.
This approach serves as a strong baseline for future machine learning applications in chemistry.
LLMs offer a practical tool for experimental chemists, streamlining synthesis planning.