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Chasing collective variables using temporal data-driven strategies.

Haochuan Chen1, Christophe Chipot1,2,3

  • 1Laboratoire International Associé Centre National de la Recherche Scientifique et University of Illinois at Urbana-Champaign, Unité Mixte de Recherche n°7019, Université de Lorraine, 54506 Vandœuvre-lès-Nancy, France.

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Summary
This summary is machine-generated.

Autoencoders (AEs) learn high-variance modes, not slow dynamics, for molecular simulations. Time-series-based models, like VAMPnets, effectively capture slow modes for accurate free-energy calculations.

Keywords:
AutoencodersVAMPnetscollective variablesfree-energy calculationsslow modes

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Machine Learning

Background:

  • Free-energy calculations rely on collective variables (CVs) representing slow molecular dynamics.
  • Autoencoders (AEs) are data-driven tools for CV discovery but their encoding is often unclear.
  • Understanding AE latent space is crucial for reliable free-energy calculations.

Purpose of the Study:

  • To evaluate the suitability of AEs and time-series-based models for discovering CVs.
  • To investigate whether AEs capture slow or high-variance molecular dynamics.
  • To compare different time-series models for identifying orthogonal CVs.

Main Methods:

  • Review of Autoencoders (AEs), time-lagged AEs (TAEs), and VAMPnets.
  • Numerical examples demonstrating AE and time-series model performance.
  • Application of state-free reversible VAMPnets (SRVs) to alanine dipeptide and trialanine isomerizations.
  • Analysis of anisotropic diffusion to explore model-committor probability relationships.

Main Results:

  • AEs identify high-variance modes, not the slow dynamics essential for free-energy calculations.
  • Time-series-based models, including modified TAEs and SRVs, successfully capture slow modes.
  • SRVs and modified TAEs generate orthogonal multidimensional CVs.
  • SRVs effectively identified CVs for N-acetyl-N'-methylalanylamide and trialanine isomerizations.

Conclusions:

  • Time-series-based models are superior to standard AEs for discovering relevant CVs in molecular simulations.
  • SRVs offer a robust method for iterative CV discovery and yield orthogonal variables.
  • Further research is needed to connect time-series models with committor probabilities.