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Related Experiment Video

Updated: Jan 9, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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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 cautious user's guide in applying HMMs to physical systems.

M Schweiger1,2, A Saurabh1,2, S Pressé1,2,3

  • 1Department of Physics, Arizona State University, Tempe, Arizona 85281, USA.

The Journal of Chemical Physics
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

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Hidden Markov models (HMMs) applied to continuous physical systems can yield misleading results. The inferred states often reflect measurement choices, not the system

Area of Science:

  • Physics
  • Complex Systems
  • Data Analysis

Background:

  • Continuous physical systems evolve in space and time.
  • Hidden Markov models (HMMs) are widely used for time series analysis.
  • HMMs were originally developed for discrete time and space.

Purpose of the Study:

  • Investigate implications of applying discrete HMMs to continuous systems.
  • Determine conditions under which HMMs provide interpretable results for physical data.
  • Examine the influence of measurement protocols on HMM inferences.

Main Methods:

  • Generated synthetic data using Langevin dynamics in an effective potential.
  • Analyzed time series data with standard HMMs.
  • Explored variations in data acquisition schemes.

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Related Experiment Videos

Last Updated: Jan 9, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Published on: April 12, 2019

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Main Results:

  • Discrete-state HMMs act as abstractions, with inferred states reflecting modeling choices.
  • Measurement protocols can significantly influence and "tune" recovered HMM states.
  • Misleading intermediate states can be reproducibly recovered even for simple potentials.

Conclusions:

  • HMMs in physical modeling require awareness of limitations due to discrete approximations.
  • Generalizations to continuous space and time are important.
  • Accurate measurement noise modeling is crucial for reliable HMM application.