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Related Concept Videos

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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Related Experiment Video

Updated: May 27, 2026

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

An off-lattice, self-learning kinetic Monte Carlo method using local environments.

Dhrubajit Konwar1, Vijesh J Bhute, Abhijit Chatterjee

  • 1Department of Chemical Engineering, Indian Institute of Technology-Kanpur, Kanpur, Uttar Pradesh 208016, India.

The Journal of Chemical Physics
|November 11, 2011
PubMed
Summary

We developed the local environment kinetic Monte Carlo (LE-KMC) method for efficient material simulations. This self-learning approach accelerates the discovery of atomic processes by focusing on local environments, improving kinetic Monte Carlo simulations.

Related Experiment Videos

Last Updated: May 27, 2026

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 Materials Science
  • Chemical Physics
  • Surface Science

Background:

  • Kinetic Monte Carlo (KMC) simulations are crucial for modeling activated processes in materials.
  • Off-lattice KMC schemes enable on-the-fly discovery of new atomic processes.
  • Efficient simulation methods are needed to capture complex material dynamics.

Purpose of the Study:

  • To introduce the Local Environment Kinetic Monte Carlo (LE-KMC) method.
  • To provide a general algorithm for self-learning KMC simulations in material systems.
  • To detail the search, classification, storage, and retrieval of processes based on local atomic environments.

Main Methods:

  • The LE-KMC method utilizes local atomic environments to describe and catalog processes.
  • It implements a self-learning approach for identifying and utilizing new atomic events.
  • The algorithm updates the system based on local information during KMC steps.

Main Results:

  • LE-KMC offers a general framework for off-lattice, self-learning KMC simulations.
  • The method efficiently describes, stores, and retrieves processes based on local environments.
  • Performance was assessed using diffusion simulations in Ag and Ag-Cu alloy films on Ag(001).

Conclusions:

  • LE-KMC provides an efficient and generalizable approach for simulating activated processes in materials.
  • The method's reliance on local environments streamlines the discovery and application of atomic processes.
  • LE-KMC demonstrates significant potential for advancing computational materials science research.