摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的研究结果在一份新的报告中讨论。根据NewsRx记者在华盛顿州里奇兰的新闻报道，研究表明：“水性有机氧化还原液流电池IES(AORFBs)由于其成本低、环境友好和可扩展性而在可再生能源储存中得到广泛应用。水溶性有机(ASO)氧化还原活性材料的迅速发现需要高效的机器收益替代物来预测电池性能。”本研究的资助者包括美国能源部(DOE)，美国西北太平洋国家实验室(PNNL)实验室指导研究与开发(LDRD)计划下的能源储存材料倡议(ESMI)，美国能源部(DOE)。新闻记者引用了太平洋Northwes T National Laboratory的一篇研究文章：“本研究提出的物理引导的持续学习(PGCL)方法，可以在解决传统机器学习中灾难性遗忘问题的同时，从新的ASO电解质中逐步学习数据。使用AORFB数据库，其中包含780 cm(2)叉指细胞模型产生的1000种潜在材料，PGCL结合了AORFB物理来优化常规学习任务的形成和训练策略，以保留以前学习的ED电池材料知识。
Abstract
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."
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