Fitting coarse-grained models to macroscopic experimental data via automatic differentiation.

Developing physics-based models for molecular simulation requires fitting many unknown parameters to diverse experimental datasets. Traditionally, this process is piecemeal and difficult to reproduce, leading to a fragmented landscape of models. Here, we establish a systematic framework for fitting coarse-grained molecular models to macroscopic experimental data by leveraging recently developed methods for computing low-variance gradient estimates with automatic differentiation. Using a widely validated DNA force field as an exemplar, we develop methods for optimizing structural, mechanical, and thermodynamic properties across a range of simulation techniques, including enhanced sampling and external forcing, spanning micro- and millisecond timescales. We highlight how automatic differentiation enables efficient sensitivity analyses that yield insights into the model parameters governing physical behaviors. We then demonstrate the broad applicability of these techniques by optimizing diverse biomolecular systems, including RNA and DNA-protein hybrid models. We show how conflict-free gradient methods from multitask learning can be adapted to impose multiple constraints simultaneously without compromising accuracy. This approach provides a foundation for transparent, reproducible, community-driven force field development, accelerating progress in molecular modeling.