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Related Concept Videos

Transition State Theory01:25

Transition State Theory

Transition-state theory, also known as activated-complex theory, provides a molecular-level explanation of reaction rates in both gas-phase and solution-phase reactions. It extends earlier kinetic models by considering the formation of a short-lived, high-energy configuration during a reaction.The progress of a chemical reaction can be represented using a reaction profile, which plots potential energy against the reaction coordinate. As two reactant molecules approach one another, their...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...

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Related Experiment Videos

Optimizing transition states via kernel-based machine learning.

Zachary D Pozun1, Katja Hansen, Daniel Sheppard

  • 1Institute for Pure and Applied Mathematics, University of California, Los Angeles, Los Angeles, California 90095-7121, USA.

The Journal of Chemical Physics
|May 16, 2012
PubMed
Summary
This summary is machine-generated.

We developed a machine learning method using support vector machines to optimize transition state theory dividing surfaces. This approach identifies reaction pathways without prior knowledge, enhancing reaction mechanism discovery and transmission coefficients.

Related Experiment Videos

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Transition State Theory (TST) is crucial for understanding chemical reaction rates.
  • Optimizing TST dividing surfaces is essential for accurate reaction mechanism analysis.
  • Current methods often rely on prior knowledge or intuition about reaction pathways.

Purpose of the Study:

  • To present a novel method for optimizing TST dividing surfaces using machine learning.
  • To develop dividing surfaces that do not require a priori information on reaction mechanisms.
  • To enhance the accuracy and efficiency of reaction rate calculations.

Main Methods:

  • Application of support vector machines for dividing surface optimization.
  • Iterative cycle of machine learning and molecular dynamics sampling for surface refinement.
  • Analysis of machine-learned surfaces to extract reaction mechanisms.

Main Results:

  • Generated optimal dividing surfaces without needing prior mechanistic insight.
  • Identified relevant low-energy saddle points on the potential energy surface.
  • Demonstrated significantly increased transmission coefficients for adatom exchange reactions compared to traditional methods.

Conclusions:

  • Machine-learned dividing surfaces provide a data-driven approach to reaction mechanism exploration.
  • The method facilitates the discovery of unexpected chemical processes.
  • This approach offers a significant improvement for complex surface reactions with multiple degrees of freedom.