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Related Experiment Video

Updated: Apr 4, 2026

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

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

Published on: December 4, 2017

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

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 programming molecular dynamics parameter 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.