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Updated: Jul 4, 2025

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开发廉价但有用的基于机器学习的模型,用于研究高合金催化剂

Chenghan Sun1, Rajat Goel1, Ambarish R Kulkarni1

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

Langmuir : the ACS journal of surfaces and colloids
|February 5, 2024
PubMed
概括

开发具有成本效益的机器学习模型,用于在没有大量计算资源的情况下进行催化. 这项研究使用密度函数理论 (DFT) 和新型描述符来预测高合金催化剂的吸附能量.

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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation

Catalytic hydrogenation of alkenes is a transition-metal catalyzed reduction of the double bond using molecular hydrogen to give alkanes. The mode of hydrogen addition follows syn stereochemistry.
The metal catalyst used can be either heterogeneous or homogeneous. When hydrogenation of an alkene generates a chiral center, a pair of enantiomeric products is expected to form. However, an enantiomeric excess of one of the products can be facilitated using an enantioselective reaction or an...
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