[目的/意义]梳理AI for Science(AI4S)领域的研究现状与发展趋势,为AI在科学研究中的应用提供前瞻性洞见。[方法/过程]以2015—2024年WoS核心数据库中AI4S领域相关文献为研究对象,将文献计量分析与BERTopic模型相结合,针对该领域的发文...[目的/意义]梳理AI for Science(AI4S)领域的研究现状与发展趋势,为AI在科学研究中的应用提供前瞻性洞见。[方法/过程]以2015—2024年WoS核心数据库中AI4S领域相关文献为研究对象,将文献计量分析与BERTopic模型相结合,针对该领域的发文趋势、发文国家、核心作者进行主题识别和发展趋势分析。[结果/结论]研究表明,AI4S领域相关文献数量呈指数型增长趋势,中国发文量居首位,但被引次数略低于美国,核心研究群体主要集中在国外学者。文章还识别出22个主题,并将其归纳为6大研究方向,重点应用领域包括人工智能与教育科技研究、医疗健康与人工智能诊断、精准农业与气候变化、材料化学与深度学习和高性能锂电池技术,其中AI与精准农业、AI与心理健康、AI与锂电池技术等是近年的研究热点。展开更多
Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in ...Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule junctions.However,the present algorithms suffer from the“uniform effect”,in which the majority events are erroneously allocated to minority ones,resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics.In this work,we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process,especially those occurring with a probability below 10%,and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%.Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.展开更多
The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-s...The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.展开更多
基金2024年度上海市重点智库课题“人工智能驱动科研范式变革研究及对上海布局AI for Science的启示”。
文摘[目的/意义]梳理AI for Science(AI4S)领域的研究现状与发展趋势,为AI在科学研究中的应用提供前瞻性洞见。[方法/过程]以2015—2024年WoS核心数据库中AI4S领域相关文献为研究对象,将文献计量分析与BERTopic模型相结合,针对该领域的发文趋势、发文国家、核心作者进行主题识别和发展趋势分析。[结果/结论]研究表明,AI4S领域相关文献数量呈指数型增长趋势,中国发文量居首位,但被引次数略低于美国,核心研究群体主要集中在国外学者。文章还识别出22个主题,并将其归纳为6大研究方向,重点应用领域包括人工智能与教育科技研究、医疗健康与人工智能诊断、精准农业与气候变化、材料化学与深度学习和高性能锂电池技术,其中AI与精准农业、AI与心理健康、AI与锂电池技术等是近年的研究热点。
基金supported by the National Key Research and Development Program of China(No.2024YFA1208103)the National Natural Science Foundation of China(Nos.22403079,22173075,22325303,21933012,and 22250003)+1 种基金the Fujian Provincial Department of Science and Technology(Nos.2022H6014 and 2023H6002)the Fundamental Research Funds for the Central Universities(Nos.20720220020 and 20720200068).
文摘Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule junctions.However,the present algorithms suffer from the“uniform effect”,in which the majority events are erroneously allocated to minority ones,resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics.In this work,we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process,especially those occurring with a probability below 10%,and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%.Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.
基金This work was supported in part by the New Generation Artificial Intelligence Development Plan of China(2015-2030)(Grant No.2021ZD0111205)the National Natural Science Foundation of China(Grant Nos.72025404,72293575 and 72074209).
文摘The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.
