Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents

• URL: http://arxiv.org/abs/2501.13299v2
• Date: Sat, 08 Feb 2025 19:56:08 GMT
• Title: Hypothesis Generation for Materials Discovery and Design Using Goal-Driven and Constraint-Guided LLM Agents
• Authors: Shrinidhi Kumbhar, Venkatesh Mishra, Kevin Coutinho, Divij Handa, Ashif Iquebal, Chitta Baral,
• Abstract summary: Large Language Models (LLMs) can be used to generate hypotheses that, once validated, can expedite materials discovery. We curated a dataset featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. We propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically.
• Score: 29.624595763508555
• License:
• Abstract: Materials discovery and design are essential for advancing technology across various industries by enabling the development of application-specific materials. Recent research has leveraged Large Language Models (LLMs) to accelerate this process. We explore the potential of LLMs to generate viable hypotheses that, once validated, can expedite materials discovery. Collaborating with materials science experts, we curated a novel dataset from recent journal publications, featuring real-world goals, constraints, and methods for designing real-world applications. Using this dataset, we test LLM-based agents that generate hypotheses for achieving given goals under specific constraints. To assess the relevance and quality of these hypotheses, we propose a novel scalable evaluation metric that emulates the process a materials scientist would use to evaluate a hypothesis critically. Our curated dataset, proposed method, and evaluation framework aim to advance future research in accelerating materials discovery and design with LLMs.

Related papers

• Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials [10.3054383984768]
  We propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials. To mitigate the shortage of available data, we create and validate unique experimental databases. We test our approach on a list of 55 compositions containing typical elements of known TCMs.
  arXiv  Detail & Related papers  (2024-11-21T11:37:05Z)
• IdeaBench: Benchmarking Large Language Models for Research Idea Generation [19.66218274796796]
  Large Language Models (LLMs) have transformed how people interact with artificial intelligence (AI) systems. We propose IdeaBench, a benchmark system that includes a comprehensive dataset and an evaluation framework. Our dataset comprises titles and abstracts from a diverse range of influential papers, along with their referenced works. Our evaluation framework is a two-stage process: first, using GPT-4o to rank ideas based on user-specified quality indicators such as novelty and feasibility, enabling scalable personalization.
  arXiv  Detail & Related papers  (2024-10-31T17:04:59Z)
• A Survey of Small Language Models [104.80308007044634]
  Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources. We present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques.
  arXiv  Detail & Related papers  (2024-10-25T23:52:28Z)
• 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)
• Data-Centric AI in the Age of Large Language Models [51.20451986068925]
  This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs) We make the key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization.
  arXiv  Detail & Related papers  (2024-06-20T16:34:07Z)
• 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)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work. ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them. We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• Large Language Models for Data Annotation and Synthesis: A Survey [49.8318827245266]
  This survey focuses on the utility of Large Language Models for data annotation and synthesis. It includes an in-depth taxonomy of data types that LLMs can annotate, a review of learning strategies for models utilizing LLM-generated annotations, and a detailed discussion of the primary challenges and limitations associated with using LLMs for data annotation and synthesis.
  arXiv  Detail & Related papers  (2024-02-21T00:44:04Z)
• A Reliable Knowledge Processing Framework for Combustion Science using Foundation Models [0.0]
  The study introduces an approach to process diverse combustion research data, spanning experimental studies, simulations, and literature. The developed approach minimizes computational and economic expenses while optimizing data privacy and accuracy. The framework consistently delivers accurate domain-specific responses with minimal human oversight.
  arXiv  Detail & Related papers  (2023-12-31T17:15:25Z)
• The Tyranny of Possibilities in the Design of Task-Oriented LLM Systems: A Scoping Survey [1.0489539392650928]
  The paper begins by defining a minimal task-oriented LLM system and exploring the design space of such systems. We discuss a pattern in our results and formulate them into three conjectures. In all, the scoping survey presents seven conjectures that can help guide future research efforts.
  arXiv  Detail & Related papers  (2023-12-29T13:35:20Z)
• Information Extraction in Low-Resource Scenarios: Survey and Perspective [56.5556523013924]
  Information Extraction seeks to derive structured information from unstructured texts. This paper presents a review of neural approaches to low-resource IE from emphtraditional and emphLLM-based perspectives.
  arXiv  Detail & Related papers  (2022-02-16T13:44:00Z)

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.