National Science Review · 2022 · 168 citations · 31 references
ComputingChemistryComputer ScienceArtificial IntelligenceMachine LearningRoboticsComputational ModelingIntelligent AgentsRobotics AutomationBayesian OptimizationExperimental Data CollectionExperimental RoboticsInterpreting Experimental DataPlatform Service ArchitectureRobotic Synthesis PlatformsScientific Data AnalysisService Platform Operations
The realization of automated chemical experiments by robots unveiled the prelude to an artificial intelligence (AI) laboratory. Several AI-based systems or robots with specific chemical skills have been demonstrated, but conducting all-round scientific research remains challenging. Here, we present an all-round AI-Chemist equipped with scientific data intelligence that is capable of performing basic tasks generally required in chemical research. Based on a service platform, the AI-Chemist is able to automatically read the literatures from a cloud database and propose experimental plans accordingly. It can control a mobile robot in-house or online to automatically execute the complete experimental process on 14 workstations, including synthesis, characterization and performance tests. The experimental data can be simultaneously analysed by the computational brain of the AI-Chemist through machine learning and Bayesian optimization, allowing a new hypothesis for the next iteration to be proposed. The competence of the AI-Chemist has been scrutinized by three different chemical tasks. In the future, the more advanced all-round AI-Chemists equipped with scientific data intelligence may cause changes to the landscape of the chemical laboratory.
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Projector augmented-wave method
Peter E. Blöchl · Physical review. B, Condensed matter · 1994 · 90.8K citations
Natural Sciences, Chemistry, Physics +17
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky et al. · 2014 · 34.3K citations