LLMatDesign: Autonomous Materials Discovery with Large Language Models
- URL: http://arxiv.org/abs/2406.13163v1
- Date: Wed, 19 Jun 2024 02:35:02 GMT
- Title: LLMatDesign: Autonomous Materials Discovery with Large Language Models
- Authors: Shuyi Jia, Chao Zhang, Victor Fung,
- Abstract summary: 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.
- Score: 5.481299708562135
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Discovering new materials can have significant scientific and technological implications but remains a challenging problem today due to the enormity of the chemical space. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials, but these methods still depend heavily on very large quantities of training data and often lack the flexibility and chemical understanding often desired in materials discovery. We introduce LLMatDesign, a novel language-based framework for interpretable materials design powered by large language models (LLMs). LLMatDesign utilizes LLM agents to translate human instructions, apply modifications to materials, and evaluate outcomes using provided tools. By incorporating self-reflection on its previous decisions, LLMatDesign adapts rapidly to new tasks and conditions in a zero-shot manner. A systematic evaluation of LLMatDesign on several materials design tasks, in silico, validates LLMatDesign's effectiveness in developing new materials with user-defined target properties in the small data regime. Our framework demonstrates the remarkable potential of autonomous LLM-guided materials discovery in the computational setting and towards self-driving laboratories in the future.
Related papers
- MatterChat: A Multi-Modal LLM for Material Science [40.34536331137755]
We introduce MatterChat, a versatile structure-aware multi-modal large language model.
We show that MatterChat significantly improves performance in material property prediction and human-AI interaction.
We also demonstrate its usefulness in applications such as more advanced scientific reasoning and step-by-step material synthesis.
arXiv Detail & Related papers (2025-02-18T18:19:36Z) - Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger [49.81945268343162]
We propose MeCo, an adaptive decision-making strategy for external tool use.
MeCo captures high-level cognitive signals in the representation space, guiding when to invoke tools.
Our experiments show that MeCo accurately detects LLMs' internal cognitive signals and significantly improves tool-use decision-making.
arXiv Detail & Related papers (2025-02-18T15:45:01Z) - Foundational Large Language Models for Materials Research [22.77591279242839]
Large Language Models (LLMs) offer opportunities to accelerate materials research through automated analysis and prediction.
Here, we present LLaMat, a family of foundational models for materials science developed through continued pretraining of LLaMA models.
We demonstrate that LLaMat excels in materials-specific NLP and structured information extraction while maintaining general linguistic capabilities.
arXiv Detail & Related papers (2024-12-12T18:46:38Z) - RA-BLIP: Multimodal Adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training [55.54020926284334]
Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks.
Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs.
In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs.
arXiv Detail & Related papers (2024-10-18T03:45:19Z) - Learning to Ask: When LLM Agents Meet Unclear Instruction [55.65312637965779]
Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone.
We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench.
We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
arXiv Detail & Related papers (2024-08-31T23:06:12Z) - HoneyComb: A Flexible LLM-Based Agent System for Materials Science [31.173615509567885]
HoneyComb is the first large language model system specifically designed for materials science.
MatSciKB is a curated, structured knowledge collection based on reliable literature.
ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science.
arXiv Detail & Related papers (2024-08-29T15:38:40Z) - From Text to Insight: Large Language Models for Materials Science Data Extraction [4.08853418443192]
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.
arXiv Detail & Related papers (2024-07-23T22:23:47Z) - Are LLMs Ready for Real-World Materials Discovery? [10.87312197950899]
Large Language Models (LLMs) create exciting possibilities for powerful language processing tools to accelerate research in materials science.
While LLMs have great potential to accelerate materials understanding and discovery, they currently fall short in being practical materials science tools.
We show relevant failure cases of LLMs in materials science that reveal current limitations of LLMs related to comprehending and reasoning over complex, interconnected materials science knowledge.
arXiv Detail & Related papers (2024-02-07T19:10:36Z) - Simultaneous Machine Translation with Large Language Models [51.470478122113356]
We investigate the possibility of applying Large Language Models to SimulMT tasks.
We conducted experiments using the textttLlama2-7b-chat model on nine different languages from the MUST-C dataset.
The results show that LLM outperforms dedicated MT models in terms of BLEU and LAAL metrics.
arXiv Detail & Related papers (2023-09-13T04:06:47Z) - CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models [74.22729793816451]
Large Language Models (LLMs) have made significant progress in utilizing tools, but their ability is limited by API availability.
We propose CREATOR, a novel framework that enables LLMs to create their own tools using documentation and code realization.
We evaluate CREATOR on MATH and TabMWP benchmarks, respectively consisting of challenging math competition problems.
arXiv Detail & Related papers (2023-05-23T17:51:52Z) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive
Critiquing [139.77117915309023]
CRITIC allows large language models to validate and amend their own outputs in a manner similar to human interaction with tools.
Comprehensive evaluations involving free-form question answering, mathematical program synthesis, and toxicity reduction demonstrate that CRITIC consistently enhances the performance of LLMs.
arXiv Detail & Related papers (2023-05-19T15:19:44Z)
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.