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Related Experiment Video

Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Bridging interpretable machine learning and large language models through direct representative selection and

Tomomi Shimazaki¹, Masanori Tachikawa¹

¹Quantum Chemistry Division, Yokohama City University, Seto 22-2, Kanazawa-Ku, Yokohama 236-0027, Kanagawa, Japan. tshima@yokohama-cu.ac.jp.

Physical Chemistry Chemical Physics : PCCP

|July 1, 2026

Summary

This study combines interpretable machine learning and large language models to uncover chemical structure-reactivity trends. The approach enhances mechanistic reasoning and aids in extracting physicochemical insights from complex datasets.

Related Experiment Videos

Last Updated: Jul 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Computational Chemistry
Machine Learning in Chemistry
Chemical Reactivity Analysis

Background:

Understanding structure-reactivity relationships is crucial for predicting chemical reactions.
Interpreting complex datasets often requires expert knowledge and can be prone to cognitive biases.
Existing machine learning models may lack transparency in their decision-making processes.

Purpose of the Study:

To develop a novel framework combining interpretable machine learning (ML) and large language models (LLMs) for analyzing chemical reaction data.
To investigate structure-reactivity trends in acrylate/methacrylate radical reactions.
To enhance the interpretability of ML models and assist in extracting mechanistic insights.

Main Methods:

Employed a modified convex clustering regression framework with direct representative selection (DRS) and direct representative prediction (DRP) for instance-level interpretability.
Utilized density functional theory (DFT) calculations to construct a dataset of acrylate/methacrylate radical reactions.
Integrated a large language model (LLM) as an assistive interpreter for mechanistic analysis.

Main Results:

The DRS/DRP framework provided instance-level interpretability, enabling the extraction of chemically meaningful insights.
LLM assistance, guided by prompt design and model size, systematically influenced the depth of mechanistic understanding.
Larger LLMs and stronger framing led to more mechanism-oriented reasoning, while smaller models yielded concise summaries.

Conclusions:

The combined interpretable ML and LLM framework offers a two-layer approach for structured extraction of physicochemical insights.
LLM assistance aids human interpretation by providing alternative perspectives and structuring mechanistic reasoning.
This approach facilitates systematic accumulation of mechanistic interpretations, paving the way for future knowledge discovery in chemical reactivity.