# Large Language Models for Predicting Corrosion Inhibition Efficiency: Scientific Application of Table Embedding on Small Datasets

> This article introduces a study using large language models and table embedding technology to predict corrosion inhibition efficiency, demonstrating the innovative application of AI in the field of materials science.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-14T04:43:17.000Z
- 最近活动: 2026-05-15T04:47:53.641Z
- 热度: 122.9
- 关键词: 大语言模型, 表格嵌入, 材料科学, 腐蚀抑制, 小样本学习, AI for Science
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-langzi0721-llmcorrosion
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-langzi0721-llmcorrosion
- Markdown 来源: floors_fallback

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## [Introduction] Large Language Models + Table Embedding: An Innovative Study on Predicting Corrosion Inhibition Efficiency with Small Datasets

This article presents a study that uses large language model (LLM) table embedding technology to predict corrosion inhibition efficiency. Addressing the pain point of data scarcity in the field of materials science, it demonstrates a new breakthrough in the scientific application of AI. The core highlight is the conversion of structured data via table embedding, leveraging the pre-trained knowledge of LLMs to achieve accurate small-sample prediction, providing a reference for corrosion inhibitor screening and other scientific fields with small datasets.

## Research Background: Challenges in Corrosion Science and AI Intervention

Corrosion causes hundreds of billions of dollars in economic losses annually. Traditional inhibitor screening relies on a large number of experiments, which are time-consuming and costly. Machine learning for predicting corrosion inhibition efficiency faces the dilemma of data scarcity, as high-quality experimental data is difficult to obtain on a large scale. This study proposes an LLM table embedding solution, providing a new idea for solving small data problems.

## Core Methodology: Innovative Application of Table Embedding Technology

The core of the study is to convert structured data such as corrosion inhibitor molecular information and experimental conditions into embedding representations understandable by LLMs. Unlike traditional manual feature engineering, LLMs use pre-trained semantic knowledge to directly process table text and structural information, reducing dependence on task-specific data through transfer learning and enabling effective small-sample learning.

## Experimental Validation: Rigorous Design and Performance Evaluation

The experimental dataset covers the chemical structures, concentrations, and material types of various inhibitors. Training/test set division is used to ensure fair evaluation. Model performance is verified using metrics such as mean squared error (MSE), R² score, and generalization ability. Ablation experiments are conducted to compare with traditional methods like molecular fingerprints and graph neural networks, proving the effectiveness of the table embedding solution.

## Scientific Significance and Application Prospects: Cross-domain Promotion Value

This study not only accelerates corrosion inhibitor screening (reducing experimental costs and time) but also provides a general methodology: using LLM semantic understanding to process scientific table data, which can be extended to fields such as drug discovery and material design. It is a typical representative of the 'foundation model + scientific application' paradigm, changing the way scientific research is conducted.

## Technical Implementation and Open Source: Promoting Reproducibility and Knowledge Dissemination

The research's open-source repository provides complete data and code, including data preprocessing, table embedding generation, and model training/evaluation processes, ensuring scientific reproducibility. The open-source code provides a reference for researchers in other fields, accelerating knowledge iteration and technology dissemination in the AI for Science domain.

## Limitations and Future Outlook: Areas for Improvement to Explore

The study has limitations such as data volume constraints (small data still limits performance), reliance on manual table structure design, and insufficient model interpretability. Future directions include integrating chemical prior knowledge, multi-modal information fusion, and active learning to guide experimental design, further improving model performance and application value.