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Related Concept Videos

Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in...
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
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Rigid Body Equilibrium Problems - II01:21

Rigid Body Equilibrium Problems - II

A rigid body is in static equilibrium when the net force and the net torque acting on the system are equal to zero.
Consider two children sitting on a seesaw, which has negligible mass. The first child has a mass (m1) of 26 kg and sits at point A, which is 1.6 meters (r1) from the pivot point B; the second child has a mass (m2) of 32 kg and sits at point C. How far from the pivot point B should the second child sit (r2) to balance the seesaw?
Equation of Rotational Dynamics01:08

Equation of Rotational Dynamics

Angular variables are introduced in rotational dynamics. Comparing the definitions of angular variables with the definitions of linear kinematic variables, it is seen that there is a mapping of the linear variables to the rotational ones. Linear displacement, velocity, and acceleration have their equivalents in rotational motion, which are angular displacement, angular velocity, and angular acceleration. Similar to the rotational variables, a mapping exists from Newton's second law of motion...

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Expression and Purification of the Human Lipid-sensitive Cation Channel TRPC3 for Structural Determination by Single-particle Cryo-electron Microscopy
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PES2MP: A Python Application for Automating Collisional Dynamics of Linear Rigid-Rotors.

Apoorv Kushwaha1, Pooja Chahal1, Habit Tatin1

  • 1Quantum Dynamics Lab, Department of Chemistry, Indian Institute of Technology Ropar, Rupnagar, India.

Journal of Computational Chemistry
|July 2, 2026
PubMed
Summary

This study presents a Python program automating collisional dynamics calculations for cold environments. It streamlines potential energy surface generation and predicts rate coefficients for interstellar medium species, reducing computation time significantly.

Keywords:
machine learningmultipole expansionpotential energy surfacequantum scatteringrate coefficients

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Published on: June 24, 2016

Area of Science:

  • Computational Chemistry and Ast rophysics
  • Molecular Dynamics and Quantum Mechanics
  • Interstellar Medium (ISM) Modeling

Background:

  • Accurate collisional rate coefficients are essential for modeling interstellar gas properties and chemical abundances.
  • Traditional methods for calculating these coefficients are computationally intensive and time-consuming.
  • The development of efficient computational tools is crucial for advancing astrochemical research.

Purpose of the Study:

  • To introduce a modular Python program with a graphical user interface (GUI) for automating collisional dynamics of rigid-rotors.
  • To streamline the generation and fitting of ab initio potential energy surfaces (PES), incorporating neural network (NN) augmentation.
  • To enable accurate and rapid prediction of state-to-state rate coefficients for astrochemical applications.

Main Methods:

  • Development of a Python-based program with a GUI for automated PES construction.
  • Utilizing multipole expansion to generate radial terms for the Molscat code.
  • Employing ab initio calculations and neural network augmentation for PES fitting and rate coefficient prediction.
  • Benchmarking calculations for C2-He/H2 systems across 11 rotational states.

Main Results:

  • The program successfully automates the generation of PES and calculation of rate coefficients, significantly reducing processing time from weeks to hours.
  • Calculated rate coefficients for C2-He/H2 demonstrate the accuracy of the ab initio and NN methods.
  • An ensemble NN model was developed for direct prediction of rate coefficients for various carbonaceous species (Cn, HCN, HCn-, HCn+, HCn N).
  • The tool provides robust and reproducible results, accelerating collisional studies.

Conclusions:

  • The developed GUI-automated program offers an efficient and general framework for accurate state-to-state rate predictions.
  • This tool significantly reduces computational costs and data processing time, facilitating broader astrochemical and molecular dynamics research.
  • The ensemble NN model provides a powerful method for predicting rate coefficients of complex carbon chains relevant to the ISM.