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Heterogeneous Catalysis01:22

Heterogeneous Catalysis

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Heterogeneous catalysis involves a catalyst in a different phase from the reactants. It is a process where the catalyst and the reactants are in distinct phases, typically solid and gas or liquid.Most heterogeneous catalysts are metals, metal oxides, or acids. The list includes transition metals like iron (Fe), cobalt (Co), nickel (Ni), palladium (Pd), platinum (Pt), chromium (Cr), manganese (Mn), tungsten (W), silver (Ag), and copper (Cu). These metals possess partially vacant d orbitals that...
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Introduction to Mechanisms of Enzyme Catalysis01:13

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Catalysis02:50

Catalysis

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The presence of a catalyst affects the rate of a chemical reaction. A catalyst is a substance that can increase the reaction rate without being consumed during the process. A basic comprehension of a catalysts’ role during chemical reactions can be understood from the concept of reaction mechanisms and energy diagrams.
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Reduction of Alkenes: Asymmetric Catalytic Hydrogenation02:17

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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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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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预训练机器学习在异质催化中的原子间潜力的挑战和机遇

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  • 1Institute of Chemical Research of Catalonia (ICIQ-CERCA), The Barcelona Institute of Science and Technology, Av. Països Catalans 16, Tarragona 43007, Spain.

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概括

机器学习原子间潜力 (MLIPs) 提供了计算催化学的范式转变,以更低的成本匹配密度函数理论 (DFT) 的准确性. 这一观点探讨了MLIP作为异质催化剂的工具,解决广泛采用的挑战.

关键词:
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 催化剂是一种催化剂.

背景情况:

  • 精确的表面反应率建模对于催化剂设计至关重要.
  • 密度函数理论 (DFT) 是原子学理解的主要计算方法.
  • 在计算上,DFT计算非常昂贵.

研究的目的:

  • 为异质催化提供最先进的机器学习原子间潜力 (MLIPs) 的概述.
  • 评估MLIPs作为催化研究的"开箱即用"工具.
  • 讨论MLIPs在民主化计算催化剂方面的潜力.

主要方法:

  • 概括不同MLIP家族及其培训过程.
  • 将预训练的MLIP模型应用于异构的催化问题.
  • 批判性地评估模型的可转移性和整合挑战.

主要成果:

  • MLIP显示出与显著降低计算成本的DFT准确度相匹配的潜力.
  • 预先训练有素的MLIP可以应用于异质的催化问题.
  • 在模型可转移性,标准化和实现可靠的预测能力方面仍然存在挑战.

结论:

  • MLIPs代表了计算催化学的重大进步.
  • 需要标准化的协议来衡量MLIP的性能.
  • 需要进一步发展,以克服MLIP广泛,可靠使用的障碍.