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用回归机器学习模型和人工阻碍算法表示的软计算在预测金属有机框架中储存的应用.

Jiamin Zhang1, Yanzhe Li2, Chuanqi Li3

  • 1SINOPEC Research Institute of Petroleum Engineering, Beijing 100101, China.

Materials (Basel, Switzerland)
|July 12, 2025
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概括

机器学习模型预测金属有机框架 (MOF) 的储能容量. 人工 lemming 算法优化随机森林 (ALA-RF) 模型表现出卓越的预测性能,确定压力是关键因素.

关键词:
人工虫算法的人工虫算法储存的储存的储存.机器学习是机器学习.金属有机框架的框架.预测 预测 预测 预测

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科学领域:

  • 材料科学 材料科学 材料科学
  • 化学工程是化学工程的重要组成部分.
  • 计算化学的计算化学

背景情况:

  • 金属有机框架 (MOF) 具有独特的特性,使其成为储存应用的前景.
  • 准确预测MOF中的储能容量对于材料选择和工艺优化至关重要.

研究的目的:

  • 开发和评估几种基于回归的机器学习模型,用于预测MOF中的储能.
  • 使用人工 lemming 算法 (ALA) 来优化机器学习模型的超参数.

主要方法:

  • 人工神经网络 (ANN),支持向量回归 (SVR),随机森林 (RF),极端学习机器 (ELM),内核极端学习机器 (KELM) 和通用回归神经网络 (GRNN) 模型的开发.
  • 使用人工 lemming 算法 (ALA) 的超参数优化.
  • 使用实验性储存数据进行模型训练和测试,通过统计指标,回归图和泰勒图进行性能评估.

主要成果:

  • 优化ALA随机森林 (ALA-RF) 模型表现出最高的预测准确度.
  • 针对ALA-RF的最佳性能指标包括R2为0.9845 (训练) 和0.9840 (测试),RMSE为0.2719 (训练) 和0.2828 (测试).
  • 压力被确定为预测MOF中储能容量的最有影响的特征.

结论:

  • ALA-RF 模型提供了一种强大而准确的方法来预测MOF中的储存.
  • 这些发现有助于对MOF进行智能选择,并优化储存的操作条件.