The Innovation (Nov 2021)
Artificial intelligence: A powerful paradigm for scientific research
- Yongjun Xu,
- Xin Liu,
- Xin Cao,
- Changping Huang,
- Enke Liu,
- Sen Qian,
- Xingchen Liu,
- Yanjun Wu,
- Fengliang Dong,
- Cheng-Wei Qiu,
- Junjun Qiu,
- Keqin Hua,
- Wentao Su,
- Jian Wu,
- Huiyu Xu,
- Yong Han,
- Chenguang Fu,
- Zhigang Yin,
- Miao Liu,
- Ronald Roepman,
- Sabine Dietmann,
- Marko Virta,
- Fredrick Kengara,
- Ze Zhang,
- Lifu Zhang,
- Taolan Zhao,
- Ji Dai,
- Jialiang Yang,
- Liang Lan,
- Ming Luo,
- Zhaofeng Liu,
- Tao An,
- Bin Zhang,
- Xiao He,
- Shan Cong,
- Xiaohong Liu,
- Wei Zhang,
- James P. Lewis,
- James M. Tiedje,
- Qi Wang,
- Zhulin An,
- Fei Wang,
- Libo Zhang,
- Tao Huang,
- Chuan Lu,
- Zhipeng Cai,
- Fang Wang,
- Jiabao Zhang
Affiliations
- Yongjun Xu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Xin Liu
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Xin Cao
- Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China
- Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Enke Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
- Sen Qian
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- Xingchen Liu
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
- Yanjun Wu
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Fengliang Dong
- National Center for Nanoscience and Technology, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Cheng-Wei Qiu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
- Junjun Qiu
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
- Keqin Hua
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China; Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
- Wentao Su
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
- Jian Wu
- Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, China
- Huiyu Xu
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
- Yong Han
- Zhejiang Provincial People’s Hospital, Hangzhou 310014, China
- Chenguang Fu
- School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Zhigang Yin
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
- Miao Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
- Ronald Roepman
- Medical Center, Radboud University, 6500 Nijmegen, the Netherlands
- Sabine Dietmann
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Marko Virta
- Department of Microbiology, University of Helsinki, 00014 Helsinki, Finland
- Fredrick Kengara
- School of Pure and Applied Sciences, Bomet University College, Bomet 20400, Kenya
- Ze Zhang
- Agriculture College of Shihezi University, Xinjiang 832000, China
- Lifu Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; Agriculture College of Shihezi University, Xinjiang 832000, China
- Taolan Zhao
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
- Ji Dai
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
- Jialiang Yang
- Geneis (Beijing) Co., Ltd, Beijing 100102, China
- Liang Lan
- Department of Communication Studies, Hong Kong Baptist University, Hong Kong, China
- Ming Luo
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China; Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou 510650, China
- Zhaofeng Liu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
- Tao An
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
- Bin Zhang
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
- Xiao He
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- Shan Cong
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
- Xiaohong Liu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- Wei Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
- James P. Lewis
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
- James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
- Qi Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Zhejiang Lab, Hangzhou 311121, China; Corresponding author
- Zhulin An
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author
- Fei Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author
- Libo Zhang
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author
- Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China; Corresponding author
- Chuan Lu
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3FL, UK; Corresponding author
- Zhipeng Cai
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA; Corresponding author
- Fang Wang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author
- Jiabao Zhang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China; University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author
- Journal volume & issue
-
Vol. 2,
no. 4
p. 100179
Abstract
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.
