首页|Data on Machine Learning Reported by Researchers at University of Maryland (Asse ssing the Accuracy of Machine Learning Interatomic Potentials In Predicting the Elemental Orderings: a Case Study of Li-al Alloys)
Data on Machine Learning Reported by Researchers at University of Maryland (Asse ssing the Accuracy of Machine Learning Interatomic Potentials In Predicting the Elemental Orderings: a Case Study of Li-al Alloys)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news originatingfrom College Park, Maryland, by Ne wsRx correspondents, research stated, “In atomistic modeling, machinelearning i nteratomic potential (MLIP) has emerged as a powerful technique for studying all oy materials.However, given that MLIPs are often trained on a limited set of ma terials, a concern remains regardingthe MLIP’s capability to make accurate pred ictions for a wide variety of phases, compositions, latticestructures, and elem ental orderings across alloy systems.”
College ParkMarylandUnited StatesN orth and Central AmericaAlloysCyborgsEmerging TechnologiesMachine LearningUniversity of Maryland
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