首页|Princeton University Reports Findings in Machine Learning (Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery)
Princeton University Reports Findings in Machine Learning (Machine Learning for Polymer Design to Enhance Pervaporation-Based Organic Recovery)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Princeton, New Jersey, by NewsRx journalists, research stated, "Pervaporation (PV) is an effec tive membrane separation process for organic dehydration, recovery, and upgradin g. However, it is crucial to improve membrane materials beyond the current perme ability-selectivity trade-off." The news reporters obtained a quote from the research from Princeton University, "In this research, we introduce machine learning (ML) models to identify high-p otential polymers, greatly improving the efficiency and reducing cost compared t o conventional trial-and-error approach. We utilized the largest PV data set to date and incorporated polymer fingerprints and features, including membrane stru cture, operating conditions, and solute properties. Dimensionality reduction, mi ssing data treatment, seed randomness, and data leakage management were employed to ensure model robustness. The optimized LightGBM models achieved RMSE of 0.44 7 and 0.360 for separation factor and total flux, respectively (logarithmic scal e). Screening approximately 1 million hypothetical polymers with ML models resul ted in identifying polymers with a predicted permeation separation index > 30 and synthetic accessibility score <3.7 for acetic acid e xtraction."
PrincetonNew JerseyUnited StatesNo rth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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