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Updated: Nov 19, 2025

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

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Accelerating coupled cluster calculations with nonlinear dynamics and supervised machine learning.

Valay Agarawal1, Samrendra Roy2, Anish Chakraborty1

  • 1Department of Chemistry, Indian Institute of Technology Bombay, Mumbai, India.

The Journal of Chemical Physics
|January 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning approach to accelerate coupled cluster calculations. By identifying key amplitudes, computational time is significantly reduced without compromising accuracy in quantum chemistry.

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Theoretical Chemistry

Background:

  • Coupled cluster theory is a standard method for high-accuracy electronic structure calculations.
  • Traditional coupled cluster methods can be computationally expensive, limiting their application to larger systems.
  • Analyzing the iteration scheme using nonlinear dynamics can reveal underlying structures in the calculations.

Purpose of the Study:

  • To develop a computationally efficient method for coupled cluster calculations.
  • To reduce the computational cost of single reference coupled cluster (SRCC) theory.
  • To maintain the accuracy of coupled cluster results while accelerating the computation.

Main Methods:

  • Nonlinear dynamics and phase space analysis were used to study the iteration scheme of SRCC.
  • A supervised machine learning model (polynomial kernel ridge regression) was employed.
  • Enslaved cluster amplitudes were mapped as a function of significant amplitudes.

Main Results:

  • Phase space analysis revealed a few dominant cluster amplitudes governing the dynamics.
  • A machine learning model successfully predicted enslaved amplitudes based on significant ones.
  • The hybrid scheme demonstrated a significant reduction in computational time.

Conclusions:

  • The hybrid machine learning approach offers a substantial speedup for coupled cluster calculations.
  • This method maintains the accuracy of traditional coupled cluster theory.
  • The findings pave the way for more efficient quantum chemistry simulations.