Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces.
Eric S MuckleyRama VasudevanBobby G SumpterRigoberto C AdvinculaIlia N IvanovPublished in: ACS applied materials & interfaces (2022)
Classic design of experiment relies on a time-intensive workflow that requires planning, data interpretation, and hypothesis building by experienced researchers. Here, we describe an integrated, machine-intelligent experimental system which enables simultaneous dynamic tests of electrical, optical, gravimetric, and viscoelastic properties of materials under a programmable dynamic environment. Specially designed software controls the experiment and performs on-the-fly extensive data analysis and dynamic modeling, real-time iterative feedback for dynamic control of experimental conditions, and rapid visualization of experimental results. The system operates with minimal human intervention and enables time-efficient characterization of complex dynamic multifunctional environmental responses of materials with simultaneous data processing and analytics. The system provides a viable platform for artificial intelligence (AI)-centered material characterization, which, when coupled with an AI-controlled synthesis system, could lead to accelerated discovery of multifunctional materials.
Keyphrases
- artificial intelligence
- big data
- data analysis
- deep learning
- machine learning
- electronic health record
- randomized controlled trial
- drug delivery
- high throughput
- endothelial cells
- small molecule
- computed tomography
- mass spectrometry
- high speed
- atomic force microscopy
- neural network
- loop mediated isothermal amplification