On the equivalence of the hybrid particle–field and Gaussian core models

Ledum, Morten; Sen, Samiran; Cascella, M.

doi:10.1063/5.0145142

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…While particle-field simulations are maturing both in terms of mathematical foundations 14,15 and open-source software implementation, 9,16,17 the parameterization of the particle-field force field remains challenging. On top of being coarse-grained, which targets the more elusive potential of mean force instead of the potential energy surface of all-atom simulations, the determination of the non-standard particle-field interactions are not straightforwardly amenable to traditional techniques, such as iterative Boltzmann inversion or force-matching.…”

Section: Introductionmentioning

confidence: 99%

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Carrer,

Cezar,

Bore

et al. 2024

Preprint

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We develop ∂-HylleraasMD (∂-HyMD), a fully end-to-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently established HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads, and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Carrer,

Cezar,

Bore

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…While particle-field simulations are maturing both in terms of mathematical foundations , and open-source software implementation, ,, the parametrization of the particle-field force field remains challenging. On top of being coarse-grained, which targets the more elusive potential of mean force instead of the potential energy surface of all-atom simulations, the determination of the nonstandard particle-field interactions are not straightforwardly amenable to traditional techniques, such as iterative Boltzmann inversion or force-matching. , The key challenge resides in calibrating the χ̃ k l -parameter matrix, which describes the mixing energy between the density of two species k and l .…”

Section: Introductionmentioning

confidence: 99%

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Carrer,

Cezar,

Bore

et al. 2024

J. Chem. Inf. Model.

Self Cite

View full text Add to dashboard Cite

We develop ∂-HylleraasMD (∂-HyMD), a fully endto-end differentiable molecular dynamics software based on the Hamiltonian hybrid particle-field formalism, and use it to establish a protocol for automated optimization of force field parameters. ∂-HyMD is templated on the recently released HylleraaasMD software, while using the JAX autodiff framework as the main engine for the differentiable dynamics. ∂-HyMD exploits an embarrassingly parallel optimization algorithm by spawning independent simulations, whose trajectories are simultaneously processed by reverse mode automatic differentiation to calculate the gradient of the loss function, which is in turn used for iterative optimization of the force-field parameters. We show that parallel organization facilitates the convergence of the minimization procedure, avoiding the known memory and numerical stability issues of differentiable molecular dynamics approaches. We showcase the effectiveness of our implementation by producing a library of force field parameters for standard phospholipids, with either zwitterionic or anionic heads and with saturated or unsaturated tails. Compared to the all-atom reference, the force field obtained by ∂-HyMD yields better density profiles than the parameters derived from previously utilized gradient-free optimization procedures. Moreover, ∂-HyMD models can predict with good accuracy properties not included in the learning objective, such as lateral pressure profiles, and are transferable to other systems, including triglycerides.

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Phase Coexistence in Hamiltonian Hybrid Particle–Field Theory Using a Multi-Gaussian Approach

Sen,

Cezar,

Ledum

et al. 2024

J. Phys. Chem. B

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This study introduces an implementation of multiple Gaussian filters within the Hamiltonian hybrid particle-field (HhPF) theory, aimed at capturing phase coexistence phenomena in mesoscopic molecular simulations. By employing a linear combination of two Gaussians, we demonstrate that HhPF can generate potentials with attractive and steric components analogous to Lennard−Jones (LJ) potentials, which are crucial for modeling phase coexistence. We compare the performance of this method with the multi-Gaussian core model (MGCM) in simulating liquid−gas coexistence for a single-component system across various densities and temperatures. Our results show that HhPF effectively captures detailed information on phase coexistence and interfacial phenomena, including microconfiguration transitions and increased interfacial fluctuations at higher temperatures. Notably, the phase boundaries obtained from HhPF simulations align more closely with those of LJ systems compared to the MGCM results. This work advances the hybrid particle-field methodology to address phase coexistence without requiring modifications to the equation of state or introducing additional interaction energy functional terms, offering a promising approach for mesoscale molecular simulations of complex systems.

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On the equivalence of the hybrid particle–field and Gaussian core models

Cited by 3 publications

References 51 publications

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics

Phase Coexistence in Hamiltonian Hybrid Particle–Field Theory Using a Multi-Gaussian Approach

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