首页|Sorbonne Universite Reports Findings in Machine Learning (Accelerating QM/MM sim ulations of electrochemical interfaces through machine learning of electronic ch arge densities)
Sorbonne Universite Reports Findings in Machine Learning (Accelerating QM/MM sim ulations of electrochemical interfaces through machine learning of electronic ch arge densities)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting originating from Paris, Franc e, by NewsRx correspondents, research stated, “A crucial aspect inthe simulatio n of electrochemical interfaces consists in treating the distribution of electro nic charge ofelectrode materials that are put in contact with an electrolyte so lution. Recently, it has been shown howa machine-learning method that specifica lly targets the electronic charge density, also known as SALTED,can be used to predict the long-range response of metal electrodes in model electrochemical cel ls.”
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