首页|University of Glasgow Reports Findings in Robotics (Algorithm- Driven Robotic Dis covery of Polyoxometalate-Scaffolding Metal- Organic Frameworks)
University of Glasgow Reports Findings in Robotics (Algorithm- Driven Robotic Dis covery of Polyoxometalate-Scaffolding Metal- Organic Frameworks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Robotics is the subject of a repo rt. According to news reporting originating in Glasgow, United Kingdom, by NewsR x journalists, research stated, “The experimental exploration of the chemical sp ace of crystalline materials, especially metal-organic frameworks (MOFs), requir es multiparameter control of a large set of reactions, which is unavoidably time -consuming and labor-intensive when performed manually. To accelerate the rate o f material discovery while maintaining high reproducibility, we developed a mach ine learning algorithm integrated with a robotic synthesis platform for closed-l oop exploration of the chemical space for polyoxometalate-scaffolding metal-orga nic frameworks (POMOFs).” The news reporters obtained a quote from the research from the University of Gla sgow, “The eXtreme Gradient Boosting (XGBoost) model was optimized by using upda ting data obtained from the uncertainty feedback experiments and a multiclass cl assification extension based on the POMOF classification from their chemical con stitution. The digital signatures for the robotic synthesis of POMOFs were repre sented by the universal chemical description language (chDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs includin g one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered w ith a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8.”
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