Statements (49)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
research
|
| gptkbp:appliesTo |
gptkb:astronomy
biology chemistry climate science materials science physics |
| gptkbp:challenge |
scalability
reproducibility data quality interpretability integration with existing workflows |
| gptkbp:enables |
drug discovery
hypothesis generation analysis of large datasets discovery of new materials automation of experiments simulation acceleration |
| gptkbp:focusesOn |
application of artificial intelligence in scientific research
|
| gptkbp:hasApplication |
climate modeling
genomics particle physics protein structure prediction materials discovery astronomical data analysis robotic laboratory automation |
| gptkbp:hasEvent |
AI for Science Conference
AI for Science Workshop |
| https://www.w3.org/2000/01/rdf-schema#label |
AI for Science
|
| gptkbp:promotion |
gptkb:government_agency
universities technology companies national laboratories |
| gptkbp:publishedIn |
gptkb:Science_Advances
gptkb:Nature_Machine_Intelligence gptkb:Journal_of_Chemical_Information_and_Modeling |
| gptkbp:relatedTo |
computational science
data science scientific computing |
| gptkbp:supportedBy |
gptkb:software
cloud computing high-performance computing open data |
| gptkbp:uses |
gptkb:machine_learning
deep learning natural language processing data mining |
| gptkbp:bfsParent |
gptkb:NeurIPS_2021
|
| gptkbp:bfsLayer |
6
|