From Text to Insight: Large Language Models for Materials Science Data Extraction
- URL: http://arxiv.org/abs/2407.16867v1
- Date: Tue, 23 Jul 2024 22:23:47 GMT
- Title: From Text to Insight: Large Language Models for Materials Science Data Extraction
- Authors: Mara Schilling-Wilhelmi, Martiño Ríos-García, Sherjeel Shabih, María Victoria Gil, Santiago Miret, Christoph T. Koch, José A. Márquez, Kevin Maik Jablonka,
- Abstract summary: The vast majority of materials science knowledge exists in unstructured natural language.
Structured data is crucial for innovative and systematic materials design.
The advent of large language models (LLMs) represents a significant shift.
- Score: 4.08853418443192
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The vast majority of materials science knowledge exists in unstructured natural language, yet structured data is crucial for innovative and systematic materials design. Traditionally, the field has relied on manual curation and partial automation for data extraction for specific use cases. The advent of large language models (LLMs) represents a significant shift, potentially enabling efficient extraction of structured, actionable data from unstructured text by non-experts. While applying LLMs to materials science data extraction presents unique challenges, domain knowledge offers opportunities to guide and validate LLM outputs. This review provides a comprehensive overview of LLM-based structured data extraction in materials science, synthesizing current knowledge and outlining future directions. We address the lack of standardized guidelines and present frameworks for leveraging the synergy between LLMs and materials science expertise. This work serves as a foundational resource for researchers aiming to harness LLMs for data-driven materials research. The insights presented here could significantly enhance how researchers across disciplines access and utilize scientific information, potentially accelerating the development of novel materials for critical societal needs.
Related papers
- MMSci: A Multimodal Multi-Discipline Dataset for PhD-Level Scientific Comprehension [59.41495657570397]
We collected a multimodal, multidisciplinary dataset from open-access scientific articles published in Nature Communications journals.
This dataset spans 72 scientific disciplines, ensuring both diversity and quality.
We created benchmarks with various tasks and settings to comprehensively evaluate LMMs' capabilities in understanding scientific figures and content.
arXiv Detail & Related papers (2024-07-06T00:40:53Z) - Systematic Task Exploration with LLMs: A Study in Citation Text Generation [63.50597360948099]
Large language models (LLMs) bring unprecedented flexibility in defining and executing complex, creative natural language generation (NLG) tasks.
We propose a three-component research framework that consists of systematic input manipulation, reference data, and output measurement.
We use this framework to explore citation text generation -- a popular scholarly NLP task that lacks consensus on the task definition and evaluation metric.
arXiv Detail & Related papers (2024-07-04T16:41:08Z) - LLMatDesign: Autonomous Materials Discovery with Large Language Models [5.481299708562135]
New materials can have significant scientific and technological implications.
Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials.
We introduce LLMatDesign, a novel framework for interpretable materials design powered by large language models.
arXiv Detail & Related papers (2024-06-19T02:35:02Z) - SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature [80.49349719239584]
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks.
SciRIFF is the first dataset focused on extracting and synthesizing information from research literature across a wide range of scientific fields.
arXiv Detail & Related papers (2024-06-10T21:22:08Z) - Construction and Application of Materials Knowledge Graph in Multidisciplinary Materials Science via Large Language Model [16.222457389133822]
This article introduces the Materials Knowledge Graph (MKG), which utilizes advanced natural language processing techniques.
MKG categorizes information into comprehensive labels such as Name, Formula, and Application, structured around a meticulously designed ontology.
By implementing network-based algorithms, MKG not only facilitates efficient link prediction but also significantly reduces reliance on traditional experimental methods.
arXiv Detail & Related papers (2024-04-03T21:46:14Z) - Quantitative knowledge retrieval from large language models [4.155711233354597]
Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences.
This paper explores the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid data analysis tasks.
arXiv Detail & Related papers (2024-02-12T16:32:37Z) - Large Language Models for Generative Information Extraction: A Survey [89.71273968283616]
Information extraction aims to extract structural knowledge from plain natural language texts.
generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation.
LLMs offer viable solutions for IE tasks based on a generative paradigm.
arXiv Detail & Related papers (2023-12-29T14:25:22Z) - Agent-based Learning of Materials Datasets from Scientific Literature [0.0]
We develop a chemist AI agent, powered by large language models (LLMs), to create structured datasets from natural language text.
Our chemist AI agent, Eunomia, can plan and execute actions by leveraging the existing knowledge from decades of scientific research articles.
arXiv Detail & Related papers (2023-12-18T20:29:58Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z) - LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities
and Future Opportunities [68.86209486449924]
Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning evaluated.
We propose AutoKG, a multi-agent-based approach employing LLMs and external sources for KG construction and reasoning.
arXiv Detail & Related papers (2023-05-22T15:56:44Z) - Large Language Models as Master Key: Unlocking the Secrets of Materials
Science with GPT [9.33544942080883]
This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science.
We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR dataset with 91.8% F1-score and extended the dataset with data published since its release.
We also designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs)
arXiv Detail & Related papers (2023-04-05T04:01:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.