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Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
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用于利用小数据而设计催化剂的自动特征工程,而无需对目标催化剂的先前了解.

Toshiaki Taniike1, Aya Fujiwara2, Sunao Nakanowatari2

  • 1Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa, 923-1292, Japan. taniike@jaist.ac.jp.

Communications chemistry
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概括

本研究介绍了自动特征工程 (AFE) 用于具有有限数据的催化剂设计. AFE对许多假设进行选,使得数据驱动的发现能够在没有事先知识的情况下实现.

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科学领域:

  • 催化剂是一种催化剂.
  • 材料 信息学 信息学
  • 机器学习 机器学习

背景情况:

  • 催化剂信息学中的描述器设计通常需要先前的知识,对有限的数据集构成挑战.
  • 开发新的催化剂通常涉及广泛的经验测试和假设生成.

研究的目的:

  • 引入适用于小型催化剂数据集的自动特征工程 (AFE) 的新技术.
  • 为了使机器学习模型开发用于催化,而不依赖于特定假设或预先存在的知识.

主要方法:

  • 通过对催化元件的物理化学性质进行数学运算,AFE产生了众多特征.
  • 为特定的催化应用提取相关特征,以计算方式选假设.
  • 积极学习与AFE和高通量实验相结合,应用于甲 (OCM) 的氧化合.

主要成果:

  • AFE在三个异质催化系统中展示了合理的回归性能:OCM,乙醇转化为布他,以及三向催化.
  • 这种技术证明有效,即使只有培训数据集被交换.
  • 积极学习在OCM的催化剂设计中成功地可视化了机器的学习过程.

结论:

  • AFE是一种用于数据驱动催化研究的多功能和强大的技术.
  • 这种方法显著推进了对完全自动化催化剂发现的追求.