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arxiv 2504.13575 v1 pith:S6GXUHIR submitted 2025-04-18 physics.comp-ph

Autodifferentiable Geometric Restraints for Enhanced Sampling Simulations with Classical and Machine Learned Force Fields

classification physics.comp-ph
keywords restraintssamplingenhancedsimulationsadvancedforcegeneralgeometric
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The use of external restraints is ubiquitous in advanced molecular simulation techniques. In general, restraints serve to reduce the configurational space that is available for sampling, thereby reducing the computational demands associated with a given simulations. Examples include the use of positional restraints in docking simulations or positional restraints in studies of catalysis. Past work has sought to couple complex restraining potentials with enhanced sampling methods, including Metadynamics or Extended Adaptive Biasing Force approaches. Here, we introduce the use of more general geometric potentials coupled with enhanced sampling methods that incorporate neural networks or spectral decomposition to achieve more efficient sampling in the context of advanced materials design.

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