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Testing a Claim about Mean: Known Population SD

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...

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AIMSWISS-Ab initio multiple spawning with informed stochastic selections.

Yorick Lassmann¹, Basile F E Curchod¹

¹Department of Chemistry, Durham University, South Road, Durham DH1 3LE, United Kingdom.

The Journal of Chemical Physics

|July 9, 2021

Summary

Ab initio multiple spawning with informed stochastic selections (AIMSWISS) reduces computational cost for molecular dynamics simulations. This new method efficiently manages trajectory basis functions, preserving accuracy in describing nonadiabatic transitions.

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Area of Science:

Quantum chemistry
Computational chemistry
Molecular dynamics

Background:

Ab initio multiple spawning (AIMS) is a robust method for simulating excited-state molecular dynamics and nonadiabatic processes.
AIMS uses trajectory basis functions (TBFs) and a spawning algorithm to model nuclear wavefunctions.
A significant challenge in AIMS is the rapid increase in TBFs during electronic state crossings, leading to computational intractability.

Purpose of the Study:

Introduce a novel, parameter-free method, ab initio multiple spawning with informed stochastic selections (AIMSWISS).
Address the computational burden associated with the growing number of TBFs in AIMS.
Maintain the accuracy of AIMS in describing nonadiabatic transitions.

Main Methods:

Developed AIMSWISS, a variation of AIMS incorporating informed stochastic selections.
Implemented a parameter-free strategy to control the number of TBFs.
Validated the method using the photodynamics of ethylene, cyclopropanone, and fulvene.

Main Results:

AIMSWISS effectively manages the number of TBFs without compromising accuracy.
The method demonstrates reliable performance in simulating the excited-state dynamics of tested molecular systems.
Provides a computationally affordable alternative for multiple spawning simulations.

Conclusions:

AIMSWISS offers a computationally efficient and accurate approach to excited-state molecular dynamics.
The parameter-free nature of AIMSWISS simplifies its application.
This method serves as a valuable tool for studying nonadiabatic processes in molecular systems.