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相关概念视频

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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在多时解决方案系统中进行吸附能量预测的3D空间学习:MTSS数据集和基于GCN的网络

Lanqi Li1, Rui Luo2, Xiaolu Chen2

  • 1Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450001, People's Republic of China.

Journal of chemical information and modeling
|September 3, 2025
PubMed
概括

这项研究引入了一个新的数据集和深度学习模型SEP-Net,用于预测动态解决方案系统中的吸附能量. SEP-Net准确地模拟复杂的溶液-溶剂相互作用,提高吸附过程的预测准确度.

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科学领域:

  • 计算化学
  • 材料科学
  • 机器学习

背景情况:

  • 目前的吸附能量预测方法与动态解决方案系统和多样化的空间配置相斗争.
  • 传统的数据集是静态的,无法捕捉动态系统随着时间的推移所探索的结构空间.

研究的目的:

  • 引入多时溶液系统 (MTSS) 数据集以进行暂时解决的吸附预测.
  • 开发一种新的深度学习模型,即SEP-Net,能够模拟解决方案级互动.

主要方法:

  • 创建一个MTSS数据集,其中包含五种溶剂的50万个暂时解析的配置和吸附能量标签.
  • 关于SEP-Net的建议,一个双通道图形网络,集成旋转不变的几何学习和分子SMILES嵌入.
  • 对 SEP-Net 与 MLP 等传统方法的性能进行实验验证.

主要成果:

  • 在已知的溶剂中,SEP-Net的平均绝对误差 (MAE) 为211.02kJ/mol,而未见的溶剂则为507.37kJ/mol.
  • SEP-Net显著优于MLP,显示出预测准确度的大幅提高 (例如,ACE溶剂的3827.33与507.37kJ/mol).

结论:

  • MTSS数据集和SEP-Net为系统层面的吸附预测建立了新的基准.
  • 几何深度学习有效地解决动态系统中溶液-溶剂和溶剂-溶剂相互作用的复杂性.