@article{99906fae3b424e33b6c4e85853e93b2b,
title = "Artificial intelligence: A powerful paradigm for scientific research",
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.",
keywords = "artificial intelligence, chemistry, deep learning, geoscience, information science, life science, machine learning, materials science, mathematics, medical science, physics",
author = "Yongjun Xu and Xin Liu and Xin Cao and Changping Huang and Enke Liu and Sen Qian and Xingchen Liu and Yanjun Wu and Fengliang Dong and Qiu, {Cheng Wei} and Junjun Qiu and Keqin Hua and Wentao Su and Jian Wu and Huiyu Xu and Yong Han and Chenguang Fu and Zhigang Yin and Miao Liu and Ronald Roepman and Sabine Dietmann and Marko Virta and Fredrick Kengara and Ze Zhang and Lifu Zhang and Taolan Zhao and Ji Dai and Jialiang Yang and Liang Lan and Ming Luo and Zhaofeng Liu and Tao An and Bin Zhang and Xiao He and Shan Cong and Xiaohong Liu and Wei Zhang and Lewis, {James P.} and Tiedje, {James M.} and Qi Wang and Zhulin An and Fei Wang and Libo Zhang and Tao Huang and Chuan Lu and Zhipeng Cai and Fang Wang and Jiabao Zhang",
note = "Funding Information: This work was partially supported by the National Key R&D Program of China ( 2018YFA0404603 , 2019YFA0704900 , 2020YFC1807000 , and 2020YFB1313700 ), the Youth Innovation Promotion Association CAS ( 2011225 , 2012006 , 2013002 , 2015316 , 2016275 , 2017017 , 2017086 , 2017120 , 2017204 , 2017300 , 2017399 , 2018356 , 2020111 , 2020179 , Y201664 , Y201822 , and Y201911 ), NSFC (nos. 11971466 , 12075253 , 52173241 , and 61902376 ), the Foundation of State Key Laboratory of Particle Detection and Electronics ( SKLPDE-ZZ-201902 ), the Program of Science & Technology Service Network of CAS ( KFJ-STS-QYZX-050 ), the Fundamental Science Center of the National Nature Science Foundation of China (nos. 52088101 and 11971466 ), the Scientific Instrument Developing Project of CAS ( ZDKYYQ20210003 ), the Strategic Priority Research Program (B) of CAS ( XDB33000000 ), the National Science Foundation of Fujian Province for Distinguished Young Scholars ( 2019J06023 ), the Key Research Program of Frontier Sciences , CAS (nos. ZDBS-LY-7022 and ZDBS-LY-DQC012 ), the CAS Project for Young Scientists in Basic Research (no. YSBR-005 ). The study is dedicated to the 10th anniversary of the Youth Innovation Promotion Association of the Chinese Academy of Sciences. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2021",
month = nov,
day = "28",
doi = "10.1016/j.xinn.2021.100179",
language = "English",
volume = "2",
journal = "The Innovation",
issn = "2666-6758",
number = "4",
}