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  3. Fitting Coarse-grained Models To Macroscopic Experimental Data Via Automatic Differentiation.
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  3. Fitting Coarse-grained Models To Macroscopic Experimental Data Via Automatic Differentiation.

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An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Fitting coarse-grained models to macroscopic experimental data via automatic differentiation.

Ryan K Krueger1, Megan C Engel2, Ryan Hausen3

  • 1Department of Applied Mathematics, School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.

Proceedings of the National Academy of Sciences of the United States of America
|April 2, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a systematic framework for fitting molecular simulation models using automatic differentiation. This approach enables reproducible and efficient optimization of biomolecular systems, accelerating force field development.

Keywords:
differentiable programmingmolecular dynamicsparameter fitting

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Area of Science:

  • Computational chemistry
  • Molecular modeling
  • Biophysics

Background:

  • Developing physics-based models for molecular simulation involves fitting numerous parameters to experimental data.
  • Traditional methods are often piecemeal, difficult to reproduce, and lead to fragmented models.

Purpose of the Study:

  • To establish a systematic framework for fitting coarse-grained molecular models to macroscopic experimental data.
  • To leverage automatic differentiation for efficient parameter optimization and sensitivity analysis.
  • To demonstrate broad applicability across diverse biomolecular systems.

Main Methods:

  • Utilized automatic differentiation for low-variance gradient estimates.
  • Optimized structural, mechanical, and thermodynamic properties of a DNA force field.
  • Applied methods across various simulation techniques and timescales (micro- to milliseconds).
  • Adapted multitask learning for simultaneous constraint imposition.

    • Main Results:

    • Successfully optimized a DNA force field for multiple properties.
    • Demonstrated efficient sensitivity analyses providing insights into parameter behavior.
    • Showcased applicability to RNA and DNA-protein hybrid models.
    • Achieved accurate simultaneous imposition of multiple constraints.

    Conclusions:

    • The developed framework enables transparent, reproducible, and community-driven force field development.
    • This systematic approach accelerates progress in molecular modeling and simulation.
    • Automatic differentiation is key to efficient and insightful model parameterization.