首页|New Machine Learning Research from University of Kentucky Described (Machine-Lea rning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns)
New Machine Learning Research from University of Kentucky Described (Machine-Lea rning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Lexington, Kentucky, by Ne wsRx correspondents, research stated, “Non-ionic deep eutectic solvents (DESs) a re non-ionic designer solvents with various applications in catalysis, extractio n, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES form ation.” The news editors obtained a quote from the research from University of Kentucky: “The search for DES relies heavily on intuition or trial-and-error processes, l eading to low success rates or missed opportunities. Recognizing that hydrogen b onds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learni ng (ML) models to discover new DES systems. We first analyze the HB properties o f 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have more imbalance between the n umbers of the two intra-component HBs and more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the c urve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical ana lysis. Finally, we validate the models using the experimental data of 34 systems .”
University of KentuckyLexingtonKentu ckyUnited StatesNorth and Central AmericaCyborgsElementsEmerging Techn ologiesGasesHydrogenInorganic ChemicalsMachine Learning
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