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Updated: Jan 12, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
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Published on: September 5, 2014

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在金属有机框架中模拟扩散使用即时概率增强采样机器学习潜力

Sudheesh Kumar Ethirajan1, Ambarish R Kulkarni1

  • 1Department of Chemical Engineering, University of California, Davis, California 95616, United States.

Journal of chemical theory and computation
|October 30, 2025
PubMed
概括

这项研究引入了一种新的主动学习方法,用于训练机器学习潜力,用于研究纳米孔状材料中的罕见事件. 增强的采样方法捕获了以前无法通过标准模拟获得的复杂扩散机制.

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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