Titlebook: Artificial Intelligence for Science (AI4S); Frontiers and Perspe Qinghai Miao,Fei-Yue Wang Book 2024 The Editor(s) (if applicable) and The

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书目名称Artificial Intelligence for Science (AI4S)影响因子(影响力)




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书目名称Artificial Intelligence for Science (AI4S)网络公开度学科排名




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书目名称Artificial Intelligence for Science (AI4S)被引频次学科排名




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书目名称Artificial Intelligence for Science (AI4S)年度引用学科排名




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书目名称Artificial Intelligence for Science (AI4S)读者反馈学科排名

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2731-3743 stem development and prospects. Through its balanced approach, the book offers readers a goal-oriented perspective, focusing on a concise presentation of the core ideas and reducing detailed descriptions978-3-031-67418-1978-3-031-67419-8Series ISSN 2731-3743 Series E-ISSN 2731-3751

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AI4S Based on Parallel Intelligence,e Parallel Intelligence perspectives for analyzing, comparing, and developing AI4S within a unified framework called HANOI-AI4S. First, a systematic introduction to Parallel Intelligence is provided, followed by the perspectives of Parallel Science, characterized by “three worlds, three ITs, three s

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AI for Mathematics,theorems and solving complex mathematical problems. These systems use techniques such as reinforcement learning, symbolic computation, genetic programming, and large language models to explore mathematical spaces and discover new patterns and relationships. AI algorithms and paradigms, like AlphaZer

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AI for Physics,experiments, such as those conducted at particle accelerators or telescopes. Machine learning techniques are being used to help physicists sift through massive amounts of data to identify patterns and anomalies that may lead to new discoveries. AI is also being applied to problems in theoretical phy

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AI for Biology, biology, is the application of AI to protein research. Among the numerous frontiers, this chapter covers topics from accurate protein structure prediction with AlphaFold to the innovative design of functional proteins with RFdiffusion and advanced single-cell analysis with scGPT. These advancements

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AI for Chemistry,erous advancements on the topic of AI for chemistry, this chapter introduces AlphaFlow, a reinforcement learning (RL)-guided self-driving lab (SDL) that integrates machine learning with automated experimentation. Additionally, it highlights the role of large language models (LLMs) in chemical resear

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AI for Material Science,pment processes. One area of focus is the prediction of new materials with desirable properties, such as high strength or conductivity, by analyzing large datasets of material properties and structures. AI is also being used to optimize material synthesis processes, helping researchers identify the

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