2022 International Joint Conference on Neural Networks (IJCNN) 2022
DOI: 10.1109/ijcnn55064.2022.9892533
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SOLIS: Autonomous Solubility Screening using Deep Neural Networks

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Cited by 9 publications
(7 citation statements)
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“…Deploying anthropomorphic robotic systems in SDLs is a promising area of research in that it enables the use of existing or standard instrumentation, empowers human collaboration for non-automated tasks, and is generally more exible. 89 Despite the increased cost and complexity, there have been several research efforts in this area, spanning applications from autonomous solubility screening, 81,82,90 photocatalysis, 20 and automated synthesis. 91 SDLs provide an exciting semistructured environment where the robotics community can transfer their methods to novel applications.…”
Section: Applying Robotics To Sdlsmentioning
confidence: 99%
“…Deploying anthropomorphic robotic systems in SDLs is a promising area of research in that it enables the use of existing or standard instrumentation, empowers human collaboration for non-automated tasks, and is generally more exible. 89 Despite the increased cost and complexity, there have been several research efforts in this area, spanning applications from autonomous solubility screening, 81,82,90 photocatalysis, 20 and automated synthesis. 91 SDLs provide an exciting semistructured environment where the robotics community can transfer their methods to novel applications.…”
Section: Applying Robotics To Sdlsmentioning
confidence: 99%
“…The need for a simulated chemistry environment for designing, developing, and evaluating articial intelligence algorithms is motivated by the recent growth in research on topics, such as automated chemistry and self-driving laboratories, 1-5 laboratory robots [6][7][8][9][10][11][12][13] and digital chemistry for materials and drug discovery. [14][15][16][17][18][19][20][21][22] Given RL's appropriateness for sequential decision making, and its ability to learn via online interactions with a physical or simulated environment without a supervised training signal, we see it as having a great potential within digital chemistry and self-driving laboratories. Within this context, recent work has demonstrated some successful applications of RL 23,24 or methods inspired by parts of RL 25 to automated chemistry.…”
Section: Introductionmentioning
confidence: 99%
“…A fundamental requirement for autonomous laboratory robots is the ability to measure different physical properties using fast and, where possible, non-invasive techniques that can be integrated into end-to-end workows. Such automated measurements allow the autonomous closed-loop optimization of physical properties for materials such as photocatalytic activity, 4 solubility, 5 and thin-lm performance. 6 Viscosity is a measure of a uid's resistance to ow caused by internal friction of uid layers during motion.…”
Section: Introductionmentioning
confidence: 99%