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Accelerating First-Principles Molecular-Dynamics Thermal Conductivity Calculations for Complex Systems.

Sandro Wieser1, Yu-Jie Cen1, Georg K H Madsen1

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|December 19, 2025
PubMed
Summary
This summary is machine-generated.

Noise reduction techniques for atomistic simulations of heat transport are analyzed. Cepstral analysis works for low-conductivity materials, but alternative methods are needed for high-conductivity systems to ensure accurate thermal conductivity calculations.

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Area of Science:

  • Computational Materials Science
  • Condensed Matter Physics
  • Nanotechnology

Background:

  • Atomistic simulations of heat transport are computationally expensive and difficult to converge.
  • Noise-reduction techniques for equilibrium molecular dynamics (MD) simulations have been developed to address these challenges.
  • InAs nanowires, with their complex structures and phonon spectra, serve as a benchmark for evaluating these techniques in quasi-1D systems.

Purpose of the Study:

  • To analyze the performance of noise-reduction strategies for atomistic heat transport simulations.
  • To evaluate the effectiveness of cepstral analysis for low- and high-thermal-conductivity systems.
  • To investigate alternative methods, including uncertainty propagation and covariance matrix contributions, for accurate error assessment.

Main Methods:

  • Benchmarking noise-reduction techniques using InAs nanowires.
  • Applying cepstral analysis to atomistic simulations of heat transport.
  • Utilizing uncertainty propagation from independent simulations, including covariance matrix contributions.
  • Integrating machine-learning interatomic potentials (MLIPs), specifically a transferable MACE potential, into the workflow.

Main Results:

  • Cepstral analysis effectively reduces computational cost and provides accurate results for low-thermal-conductivity systems without data discarding.
  • Cepstral analysis significantly underestimates thermal conductivity in high-thermal-conductivity systems.
  • Including covariance matrix contributions is crucial for quantitative error assessment in thermal conductivity calculations.
  • The combination of noise-reduction strategies and MLIPs offers an accelerated and robust simulation workflow.

Conclusions:

  • Cepstral analysis is a valuable tool for specific material types but requires complementary methods for others.
  • Accurate assessment of thermal conductivity in complex materials necessitates careful error analysis, including covariance.
  • Machine-learning potentials significantly enhance the efficiency and applicability of these simulation techniques across diverse materials.