首页|Findings from Pacific Northwest National Laboratory Update Understanding of Mach ine Learning (Physics-guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance)
Findings from Pacific Northwest National Laboratory Update Understanding of Mach ine Learning (Physics-guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Machine Learning are discussed in a new report. According to news reporting from Richland, Washin gton, by NewsRx journalists, research stated, "Aqueous organic redox flow batter ies (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness, and scalability. The rapid discovery of aqueo us soluble organic (ASO) redoxactive materials necessitates efficient machine l earning surrogates for predicting battery performance." Funders for this research include United States Department of Energy (DOE), Ener gy Storage Materials Initiative (ESMI) under the Laboratory Directed Research an d Development (LDRD) program at Pacific Northwest National Laboratory (PNNL), Un ited States Department of Energy (DOE). The news correspondents obtained a quote from the research from Pacific Northwes t National Laboratory, "The physics-guided continual learning (PGCL) method prop osed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Usin g an AORFB database with a thousand potential materials generated by a 780 cm(2) interdigitated cell model, PGCL incorporates AORFB physics to optimize the cont inual learning task formation and training strategies to retain previously learn ed battery material knowledge."
RichlandWashingtonUnited StatesNor th and Central AmericaCyborgsEmerging TechnologiesMachine LearningPacifi c Northwest National Laboratory
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