Scientific discovery in the age of artificial intelligence
By Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac and 20 more. Nature, 2023
1,745 citations recorded by the source corpus.
Subfields: Biophysics, Materials Chemistry, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/f08060425aa8a212d74185ee23a08329b89abcd2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
In Materials Chemistry
In Molecular Biology
Citations in the corpus
- Large Language Models on Graphs: A Comprehensive Survey (2023, background): “Synthesis designs start from available molecules and involve planning a sequence of steps that can finally produce a desired chemical compound through a series of reactions [199].”
- Can large language models provide useful feedback on research papers? A large-scale empirical analysis (2023, background): “Nevertheless, our proposed framework proves to be helpful and aligns well with comments brought up by human reviewers, demonstrating the potential of incorporating LLM in the scientific evaluation process, echoing prior works arguing for AI in the scientific process 50 .”
- Characterizing LLM scientific concept generation: A multi-dimensional measurement study (2026, unknown): “Several studies have explored LLM capabilities in scientific ideation and concept generation [1,25,26].”