Go with the flow: deep learning methods for autonomous viscosity estimations

Walker, Michael; Pizzuto, Gabriella; Fakhruldeen, Hatem; Cooper, Andrew I.

doi:10.1039/d3dd00109a

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…69 In another example, authors used visual feedback to train a 3D-CNN model for viscosity estimation of fluids. 70 At a lower level, the robot also requires visual and kinesthetic perceptual feedback in order to perform manipulation tasks successfully and robustly.…”

Section: Perception and Computer Visionmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

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Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with the autonomization of experiment planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review article provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research, and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

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Section: Perception and Computer Visionmentioning

confidence: 99%

Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Regional developers’ community accelerates laboratory automation

Kato,

Horinouchi,

Ozaki

et al. 2024

SLAS Technology

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Self-Driving Laboratories for Chemistry and Materials Science

Tom,

Schmid,

Baird

et al. 2024

Chem. Rev.

View full text Add to dashboard Cite

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

show abstract

Go with the flow: deep learning methods for autonomous viscosity estimations

Abstract: An autonomous viscosity estimation using a dexterous dual-armed collaborative robot and a three dimensional convolutional neural network model that strongly outperforms human abilities.

Cited by 4 publications

References 37 publications

Self-Driving Laboratories for Chemistry and Materials Science

Self-Driving Laboratories for Chemistry and Materials Science

Regional developers’ community accelerates laboratory automation

Self-Driving Laboratories for Chemistry and Materials Science

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