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Catalysts are essential for accelerating chemical reactions and enhancing selectivity, which is crucial for the sustainable production of energy, materials, and bioactive compounds. Catalyst discovery is fundamental yet challenging in…

Computational Engineering, Finance, and Science · Computer Science 2025-02-20 Yuanyuan Xu , Hanchen Wang , Wenjie Zhang , Lexing Xie , Yin Chen , Flora Salim , Ying Zhang , Justin Gooding , Toby Walsh

In the pursuit of novel catalyst development to address pressing environmental concerns and energy demand, conventional design and optimization methods often fall short due to the complexity and vastness of the catalyst parameter space. The…

Chemical Physics · Physics 2024-02-08 Nung Siong Lai , Yi Shen Tew , Xialin Zhong , Jun Yin , Jiali Li , Binhang Yan , Xiaonan Wang

Large Language Models (LLMs) have seen rapid advancements in recent years, with models like ChatGPT and DeepSeek, showcasing their remarkable capabilities across diverse domains. While substantial research has been conducted on LLMs in…

Machine Learning · Computer Science 2026-03-31 Licheng Zhang , Bach Le , Naveed Akhtar , Siew-Kei Lam , Tuan Ngo

The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large…

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic…

With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved in experimental design,…

Cutting-edge Artificial Intelligence (AI) techniques keep reshaping our view of the world. For example, Large Language Models (LLMs) based applications such as ChatGPT have shown the capability of generating human-like conversation on…

Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional…

Algorithm design is crucial for effective problem-solving across various domains. The advent of Large Language Models (LLMs) has notably enhanced the automation and innovation within this field, offering new perspectives and promising…

Machine Learning · Computer Science 2026-01-06 Fei Liu , Yiming Yao , Ping Guo , Zhiyuan Yang , Zhe Zhao , Xi Lin , Xialiang Tong , Kun Mao , Zhichao Lu , Zhenkun Wang , Mingxuan Yuan , Qingfu Zhang

Large Language Models (LLMs) have transformed numerous domains by providing advanced capabilities in natural language understanding, generation, and reasoning. Despite their groundbreaking applications across industries such as research,…

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The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an…

Materials Science · Physics 2023-12-12 Rachel K. Luu , Markus J. Buehler

Large Language Models (LLMs) have revolutionized the process of customer engagement, campaign optimization, and content generation, in marketing management. In this paper, we explore the transformative potential of LLMs along with the…

Computation and Language · Computer Science 2025-01-22 Raha Aghaei , Ali A. Kiaei , Mahnaz Boush , Javad Vahidi , Mohammad Zavvar , Zeynab Barzegar , Mahan Rofoosheh

Large language models (LLMs) are rapidly transforming materials science. This review examines recent LLM applications across the materials discovery pipeline, focusing on three key areas: mining scientific literature , predictive modelling,…

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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…

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With the rapid evolution of global autonomous driving technology, the demand for its core sensing hardware, Light Detection and Ranging (LiDAR), is escalating. As the light source part of the LiDAR system, lasers, particularly the…

There have been rapid advancements in the capabilities of large language models (LLMs) in recent years, greatly revolutionizing the field of natural language processing (NLP) and artificial intelligence (AI) to understand and interact with…

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Within the rapidly evolving domain of Electronic Design Automation (EDA), Large Language Models (LLMs) have emerged as transformative technologies, offering unprecedented capabilities for optimizing and automating various aspects of…

Machine Learning · Computer Science 2025-01-17 Jingyu Pan , Guanglei Zhou , Chen-Chia Chang , Isaac Jacobson , Jiang Hu , Yiran Chen

Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundamentally redefining research processes and human-AI…

Computation and Language · Computer Science 2025-09-18 Tianshi Zheng , Zheye Deng , Hong Ting Tsang , Weiqi Wang , Jiaxin Bai , Zihao Wang , Yangqiu Song

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language tasks, marking significant strides towards general artificial intelligence. While general artificial intelligence is leveraged by…

Computation and Language · Computer Science 2023-10-31 Yizhe Yang , Huashan Sun , Jiawei Li , Runheng Liu , Yinghao Li , Yuhang Liu , Heyan Huang , Yang Gao

Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials…

Materials Science · Physics 2024-11-20 Siyu Liu , Tongqi Wen , A. S. L. Subrahmanyam Pattamatta , David J. Srolovitz
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