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Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
Entropy and Solvation02:05

Entropy and Solvation

The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ ≥ 15); an...
Fast Fourier Transform01:10

Fast Fourier Transform

The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

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Published on: April 8, 2020

Accelerated conformational entropy calculations using graphic processing units.

Qian Zhang1, Junmei Wang, Ginés D Guerrero

  • 1Institute of Functional Nano & Soft Materials-FUNSOM and Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Soochow University, Suzhou, Jiangsu 215123, China.

Journal of Chemical Information and Modeling
|July 19, 2013
PubMed
Summary
This summary is machine-generated.

Calculating protein conformational entropy is now faster using a new GPU-based method that computes solvent accessible surface area (SASA). This approach accelerates entropy calculations, significantly reducing computational time for molecular modeling.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Molecular Modeling

Background:

  • Conformational entropy calculation is crucial for understanding protein dynamics and function.
  • Traditional methods like normal-mode analysis (NMA) and quasi-harmonic analysis (QHA) are computationally intensive and time-consuming.

Purpose of the Study:

  • To develop a faster method for computing conformational entropy.
  • To implement a GPU-accelerated algorithm for calculating solvent accessible surface area (SASA) for individual atoms.

Main Methods:

  • A novel solvent accessible surface area (SASA) based model was employed to compute conformational entropy.
  • A GPU-based method, MURCIA (Molecular Unburied Rapid Calculation of Individual Areas), was developed to accelerate SASA calculations.
  • MURCIA utilizes two kernels (K1 and K2) with different performance characteristics depending on protein size.

Main Results:

  • The MURCIA algorithm achieved good results across four diverse protein datasets.
  • The GPU-accelerated version demonstrated a speedup of approximately 600 times compared to the previous sequential algorithm for proteins up to 10⁵ atoms.
  • Both K1 and K2 kernels provided comparable results, with K1 outperforming K2 for smaller proteins and vice versa.

Conclusions:

  • The developed GPU-accelerated SASA-based method significantly enhances the speed of conformational entropy calculations.
  • MURCIA offers a computationally efficient alternative to traditional methods for large-scale molecular modeling and biophysical studies.
  • This advancement facilitates more rapid and extensive exploration of protein conformational landscapes.