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Updated: Sep 11, 2025

Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid
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Vibrational Spectra of a N719-Chromophore/Titania Interface from Empirical-Potential Molecular-Dynamics Simulation, Solvated by a Room Temperature Ionic Liquid

Published on: January 25, 2020

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Q-DFTNet:一种基于化学的神经网络框架,可以通过DFT驱动的QM9数据来预测分子双极时刻

Dennis Delali Kwesi Wayo1, Mohd Zulkifli Bin Mohamad Noor1, Masoud Darvish Ganji2

  • 1Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, Malaysia.

Journal of computational chemistry
|August 13, 2025
PubMed
概括

图形神经网络 (GNN) GraphConv 证明了在预测分子二极点时刻方面具有最佳的准确性和效率. 这种化学信息神经网络 (ChINN) 框架,Q-DFTNet,为量子化学应用提供了强大的基线.

关键词:
QM9 数据集 QM9 数据集基于化学信息的机器学习.两极运动时刻是双极运动时刻.图形神经网络是一个神经网络.

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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

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High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water08:48

High-Resolution Neutron Spectroscopy to Study Picosecond-Nanosecond Dynamics of Proteins and Hydration Water

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