Robotics & Machine Learning Daily News2024,Issue(MAY.15) :21-22.

Findings from Southwest Petroleum University Update Knowledge of Machine Learnin g (A Prediction Model for Co2/co Adsorption Performance On Binary Alloys Based O n Machine Learning)

Robotics & Machine Learning Daily News2024,Issue(MAY.15) :21-22.

Findings from Southwest Petroleum University Update Knowledge of Machine Learnin g (A Prediction Model for Co2/co Adsorption Performance On Binary Alloys Based O n Machine Learning)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting out of Chengdu, People’s Republic of Chi na, by NewsRx editors, research stated, “Despite the rapid development of comput ational methods, including density functional theory (DFT), predicting the perfo rmance of a catalytic material merely based on its atomic arrangements remains c hallenging. Although quantum mechanics-based methods can model ‘real’ materials with dopants, grain boundaries, and interfaces with acceptable accuracy, the hig h demand for computational resources no longer meets the needs of modern scienti fic research.”

Key words

Chengdu/People’s Republic of China/Asi a/Alloys/Cyborgs/Emerging Technologies/Machine Learning/Southwest Petroleum University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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