首页|Data from Peking University Provide New Insights into Machine Learning (Machine Learning Force Field-aided Cluster Expansion Approach To Phase Diagram of Alloye d Materials)
Data from Peking University Provide New Insights into Machine Learning (Machine Learning Force Field-aided Cluster Expansion Approach To Phase Diagram of Alloye d Materials)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators discuss new findings in Machine Learning. According to news reportingout of Beijing, People’s Republic of China, by NewsRx editors, research stated, “First-principles approachesbased on density functional theory (DFT) have played important roles in the theoretic al study of multicomponentalloyed materials. Considering the highly demanding c omputational cost of direct DFT-basedsampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonianmodels with DFT accuracy for efficient simulation of alloyed systems with configurational disor der.”
BeijingPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningPeking University
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