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Related Concept Videos

Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Random Sampling Method01:09

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Sampling materials are classified into three main types: solid, liquid, and gas.
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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A parallel, distributed memory implementation of the adaptive sampling configuration interaction method.

David B Williams-Young1, Norm M Tubman2, Carlos Mejuto-Zaera3

  • 1Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA.

The Journal of Chemical Physics
|June 1, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new parallel implementation for adaptive sampling configuration interaction (ASCI), a selected configuration interaction (sCI) method. The efficient parallelization enables the largest variational ASCI calculation to date for quantum systems.

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Area of Science:

  • Quantum chemistry
  • Computational physics
  • High-performance computing

Background:

  • Quantum system simulations utilize diverse methods, with coupled cluster and selected configuration interaction (sCI) being prominent.
  • Advancements in high-performance computing (HPC) have driven the adaptation of many quantum simulation methods.
  • Development of sCI methods for massively parallel architectures remains an underexplored area.

Purpose of the Study:

  • To present a parallel, distributed memory implementation of the adaptive sampling configuration interaction (ASCI) approach for sCI methods.
  • To address critical parallelization challenges in determinant search, selection, Hamiltonian formation, and eigenvalue calculations within ASCI.
  • To enable larger and more complex quantum system simulations on parallel computing resources.

Main Methods:

  • Parallel, distributed memory implementation of the adaptive sampling configuration interaction (ASCI) method.
  • Application of memory-efficient determinant constraints for load balancing during the determinant search.
  • Utilizing variational eigenvalue calculations for the ASCI method on massively parallel systems.

Main Results:

  • Demonstrated near-optimal speedup for ASCI calculations on up to 16,384 CPUs.
  • Successfully performed the largest variational ASCI calculation to date for the Cr2 molecule (24 electrons, 30 orbitals) involving up to 3x10^8 determinants.
  • Validated the efficiency and scalability of the parallel ASCI implementation.

Conclusions:

  • The developed parallel ASCI implementation significantly advances the capability to simulate large quantum systems.
  • This work overcomes key parallelization hurdles, making sCI methods more accessible for HPC environments.
  • The presented approach paves the way for tackling even larger and more complex quantum mechanical problems.