Robotics & Machine Learning Daily News2024,Issue(Jun.10) :111-112.

Research Data from University of Oulu Update Understanding of Machine Learning ( Polarizability Models for Simulations of Finite Temperature Raman Spectra From M achine Learning Molecular Dynamics)

来自欧鲁大学的研究数据更新了对机器学习的理解(来自机器学习分子动力学的有限温度拉曼光谱模拟的极化率模型)

Robotics & Machine Learning Daily News2024,Issue(Jun.10) :111-112.

Research Data from University of Oulu Update Understanding of Machine Learning ( Polarizability Models for Simulations of Finite Temperature Raman Spectra From M achine Learning Molecular Dynamics)

来自欧鲁大学的研究数据更新了对机器学习的理解(来自机器学习分子动力学的有限温度拉曼光谱模拟的极化率模型)

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新数据在一份新的报告中呈现。根据NewsRx Journalis TS在芬兰奥卢的新闻报道,研究表明:“拉曼光谱是一种强大的非破坏性方法,广泛用于研究固体或分子的振动性质。有限温度拉曼光谱的模拟依赖于获得分子动力学轨迹上的极化率,如果从第一原理计算,这在计算上是非常困难的。”

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Fresh data on Machine Learning are presented in a new report. According to news reporting from Oulu, Finland, by NewsRx journalis ts, research stated, “Raman spectroscopy is a powerful and nondestructive method that is widely used to study the vibrational properties of solids or molecules. Simulations of finite-temperature Raman spectra rely on obtaining polarizabilit ies along molecular-dynamics trajectories, which is computationally highly deman ding if calculated from first principles.”

Key words

Oulu/Finland/Europe/Cyborgs/Emerging Technologies/Machine Learning/Molecular Dynamics/Physics/University of Oulu

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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