Artificial intelligence: A powerful paradigm for scientific research
Open accessAbstract
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The rapid growth of AI has accelerated discoveries across diverse scientific fields and affected every work environment and is reshaping the labor market with unprecedented speed and scale, with 40% of the global workforce expected to require AI.
“The rapid growth of AI has accelerated discoveries across diverse scientific fields and affected every work environment [62–65] and is reshaping the labor market with unprecedented speed and scale, with 40% of the global workforce expected to require AI, enhancing the need for significant AI upskilling or reskilling [66].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [64]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe use of DeepMind to solve the protein folding problem is one of the most impressive examples of AI in research.
“The use of DeepMind to solve the protein folding problem is perhaps one of the most impressive example of this type of application of AI in research, but there are many others [2, 63, 64].”
From Autonomous artificial intelligence, scientific research, and human values (2026) · cites this paper as [64]
AlphaFold combines deep learning innovations to predict three-dimensional protein structures at or near experimental scale resolution.
“AlphaFold has combined numerous deep learning innovations to predict the three-dimensional (3D) structures of proteins at or near experimental scale resolution”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [2]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 202 · Cited by 1,726
Research works printed but not linked: 2
- [15]Real E. Moore S. Selle A. Large-scale evolution of image classifiers International Conference on Machine Learning 2017 PMLR 2902 2911
- [38]Shuford E.H. Some Bayesian learning processes. Technical documentary report. United States. Air force. Systems command Electron. Syst. Division 86 1963 1 39 14131123
- Authors
- Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., Qiu, J., Hua, K., Su, W., Wu, J., Xu, H., Han, Y., Fu, C., Yin, Z., Liu, M., Roepman, R., Dietmann, S., Virta, M., Kengara, F., Zhang, Z., Zhang, L., Zhao, T., Dai, J., Yang, J., Lan, L., Luo, M., Liu, Z., An, T., Zhang, B., He, X., Cong, S., Liu, X., Zhang, W., Lewis, J. P., Tiedje, J. M., Wang, Q., An, Z., Wang, F., Zhang, L., Huang, T., Lu, C., Cai, Z., Wang, F., & Zhang, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Shanghai Medical College of Fudan University (CN)
- National Center for Nanoscience and Technology (CN)
- National University of Singapore (SG)
- Dalian Polytechnic University (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Peking University Third Hospital (CN)
- Zhejiang Provincial People's Hospital (CN)
- Zhejiang University (CN)
- Radboud University Nijmegen (NL)
- Washington University in St. Louis (US)
- University of Helsinki (FI)
- Bomet University (KE)
- Shihezi University (CN)
- Hong Kong Baptist University (HK)
- Michigan State University (US)
- Georgia State University (US)
- Corresponding author
- Wang, Qi
- An, Zhulin
- Wang, Fei
- Zhang, Libo
- Huang, Tao
- Cai, Zhipeng
- Zhang, Jiabao
- ORCID
- Xu, Yongjun
- Liu, Xin
- Cao, Xin
- Huang, Changping
- Liu, Enke
- Liu, Xingchen
- Wu, Yanjun
- Dong, Fengliang
- Qiu, Cheng-Wei
- Qiu, Junjun
- Hua, Keqin
- Su, Wentao
- Wu, Jian
- Xu, Huiyu
- Han, Yong
- Fu, Chenguang
- Yin, Zhigang
- Liu, Miao
- Roepman, Ronald
- Dietmann, Sabine
- Virta, Marko
- Kengara, Fredrick
- Zhang, Ze
- Zhang, Lifu
- Zhao, Taolan
- Dai, Ji
- Yang, Jialiang
- Lan, Liang
- An, Tao
- He, Xiao
- Cong, Shan
- Liu, Xiaohong
- Zhang, Wei
- Lewis, James P.
- Tiedje, James M.
- Wang, Qi
- An, Zhulin
- Wang, Fei
- Zhang, Libo
- Huang, Tao
- Cai, Zhipeng
- Zhang, Jiabao
- Venue
- The Innovation
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 2
- Issue
- 4
- Pages
- 100179
- ISSN
- 2666-6758 (ISSN-L)
- PMC
- PMC8633405
- OpenAlex
- W3210165781
- Cited by
- 1,726
- References
- 202
- Citations by year
- 2026: 296 · 2025: 574 · 2024: 520 · 2023: 242 · 2022: 89 · 2021: 3 · 2020: 1
- FWCI
- 15.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Innovation (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Työväentutkimus Vuosikirja (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Science Fund for Distinguished Young Scholars of Fujian Province (2019J06023)
- Chinese Academy of Sciences (ZDBS-LY-DQC012, ZDBS-LY-7022, YSBR-005, KFJ-STS-QYZX-050)
- NSFC (61902376, 52173241, 12075253, 11971466)
- State Key Laboratory of Particle Detection and Electronics (SKLPDE-ZZ-201902)
- National Key Research and Development Program of China (2018YFA0404603, 2019YFA0704900, 2020YFB1313700, 2020YFC1807000)
- Youth Innovation Promotion Association of the Chinese Academy of Sciences
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- artificial intelligence, machine learning, high-throughput data analysis, scientific discovery, medical sciences, earth sciences, information science, mathematics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review