Robotics & Machine Learning Daily News2024,Issue(Jul.25) :11-11.

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)

Robotics & Machine Learning Daily News2024,Issue(Jul.25) :11-11.

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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Abstract

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.”

Key words

Beijing/People’s Republic of China/Asi a/Cyborgs/Emerging Technologies/Machine Learning/Peking University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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