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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...
Reaction Mechanisms: The Steady-State Approximation01:26

Reaction Mechanisms: The Steady-State Approximation

The steady-state approximation, also referred to as the quasi-steady-state approximation to differentiate it from a true steady state, is a widely used method for simplifying calculations in complex reaction mechanisms. This approach is particularly useful when dealing with multi-step reactions that involve reverse reactions or several steps, which can significantly increase mathematical complexity and make the reactions nearly unsolvable analytically.The steady-state approximation operates on...
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...
Reaction Mechanisms: Rate-limiting Step Approximation01:29

Reaction Mechanisms: Rate-limiting Step Approximation

The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...

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Related Experiment Video

Updated: Jun 2, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Steering Langevin Dynamics toward Transition States Using Collective-Variable-Free Resampling.

Michael Ketter1, Georg K H Madsen1

  • 1Institute of Materials Chemistry, TU Wien, A-1060 Vienna, Austria.

Journal of Chemical Theory and Computation
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

This study adapts the stochastic saddle point dynamics (SSPD) algorithm to efficiently explore chemical reaction pathways. The enhanced method aids in understanding complex molecular processes and reaction dynamics without predefined variables.

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

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Last Updated: Jun 2, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Area of Science:

  • Computational Chemistry
  • Chemical Physics
  • Materials Science

Background:

  • Understanding chemical reactivity requires exploring potential energy surfaces and transition states.
  • Sampling these regions is crucial for elucidating atomic processes.
  • Ideally, this sampling should not require predefined collective variables.

Purpose of the Study:

  • To adapt and evaluate the stochastic saddle point dynamics (SSPD) algorithm for sampling transition-state regions.
  • To demonstrate the algorithm's performance across diverse and complex chemical systems.
  • To integrate SSPD with machine learning potentials for enhanced efficiency.

Main Methods:

  • Adaptation of the stochastic saddle point dynamics (SSPD) algorithm.
  • Constraining configuration space using negative Hessian eigenvalues.
  • Application to model potentials, molecular clusters, and surface reactions.
  • Integration with machine-learned interatomic potentials.

Main Results:

  • Efficient sampling of isomerization and decomposition reactions.
  • Successful application to CO dissociation on a Co(001) surface, with and without water.
  • Demonstration of SSPD's ability to handle complex systems at finite temperatures.
  • Validation of the approach for reactions with unknown energetic or entropic contributions.

Conclusions:

  • SSPD provides a versatile framework for sampling transition states in complex systems.
  • The adapted algorithm efficiently explores reaction pathways without predefined collective variables.
  • This method is valuable for studying chemical reactivity influenced by both energetic and entropic factors.