A prompt-engineered large language model, deep learning workflow for materials classification

Liu, Siyu; Wen, Tongqi; Pattamatta, A.S.L. Subrahmanyam; Srolovitz, David J.

doi:10.1016/j.mattod.2024.08.028

Materials Today

2024

DOI: 10.1016/j.mattod.2024.08.028

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A prompt-engineered large language model, deep learning workflow for materials classification

Siyu Liu,

Tongqi Wen,

A.S.L. Subrahmanyam Pattamatta

et al.

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2024

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Cited by 3 publications

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Large-language models: The game-changers for materials science research

Yu,

Ran,

Liu

2024

Artificial Intelligence Chemistry

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Large-language models: The game-changers for materials science research

Yu,

Ran,

Liu

2024

Artificial Intelligence Chemistry

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Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

Jacobs,

Schultz,

Scourtas

et al. 2024

Mach. Learn.: Sci. Technol.

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One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

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Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

Wang,

Feng,

et al. 2024

Energies

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Smart mining solutions currently suffer from inadequate big data support and insufficient AI applications. The main reason for these limitations is the absence of a comprehensive industrial internet cloud platform tailored for the coal industry, which restricts resource integration. This paper presents the development of an innovative platform designed to enhance safety, operational efficiency, and automation in fully mechanized coal mining in China. This platform integrates cloud edge computing, real-time data processing, and AI-driven analytics to improve decision-making and maintenance strategies. Several AI models have been developed for the proactive maintenance of comprehensive mining face equipment, including early warnings for periodic weighting and the detection of common faults such as those in the shearer, hydraulic support, and conveyor. The platform leverages large-scale knowledge graph models and Graph Retrieval-Augmented Generation (GraphRAG) technology to build structured knowledge graphs. This facilitates intelligent Q&A capabilities and precise fault diagnosis, thereby enhancing system responsiveness and improving the accuracy of fault resolution. The practical process of implementing such a platform primarily based on open-source components is summarized in this paper.

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A prompt-engineered large language model, deep learning workflow for materials classification

Cited by 3 publications

References 54 publications

Large-language models: The game-changers for materials science research

Large-language models: The game-changers for materials science research

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

Development of an Intelligent Coal Production and Operation Platform Based on a Real-Time Data Warehouse and AI Model

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