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1. Entropy-based methods for formulating bottom-up ultra-coarse-grained models

   https://doi.org/10.1063/5.0244427  

   Sahrmann, Patrick_G; Voth, Gregory_A (January 2025, The Journal of Chemical Physics)

   Bottom-up coarse-grained (CG) modeling is an effective means of bypassing the limited spatiotemporal scales of conventional atomistic molecular dynamics while retaining essential information from the atomistic model. A central challenge in CG modeling is the trade-off between accuracy and efficiency, as the inclusion of often pivotal many-body interaction terms in the CG force-field renders simulation markedly slower than simple pairwise models. The Ultra Coarse-Graining (UCG) method incorporates many-body terms through discrete internal state variables that modulate the CG force-field according to, e.g., changes in local environment when substantial chemical heterogeneities exist. However, assigning optimal internal states systematically from atomistic simulation data, as well as the practical application of bottom-up UCG theory to biomolecular systems, remain open problems. We develop two synergistic methods to aid in the development of UCG models that can capture inhomogeneities in atomistic systems such as those induced by phase coexistence. The first method establishes the systematic construction of UCG force-fields from a relative entropy minimization principle, while the second method utilizes machine-learning to obtain optimal local order parameters for enhanced model efficiency and transferability. We apply these methods to a methanol liquid–vapor interface and the ripple phase of a 1,2-dipalmitoyl-sn-glycero-3-phosphocholine lipid bilayer and demonstrate that UCG modeling alone recapitulates aspects of phase coexistence that are otherwise not observed in CG modeling. 

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2. Coarse-Grained Force Field Calibration Based on Multi-Objective Bayesian Optimization to Simulate Water Diffusion in Poly-ɛ-caprolactone

   https://doi.org/10.1021/acs.jpca.0c01939  

   Sestito, Jesse M.; Thatcher, Mary L; Shu, Leshi; Harris, Tequila A.; Wang, Yan (June 2020, The Journal of Physical Chemistry A)

   Molecular dynamics at the atomistic scale is increasingly being used to predict material properties and speed up the materials design and development process. However, the accuracy of molecular dynamics predictions is sensitively dependent on the force fields. In the traditional force field calibration process, a specific property, predicted by the model, is compared with the experimental observation and the force field parameters are adjusted to minimize the difference. This leads to the issue that the calibrated force fields are not generic and robust enough to predict different properties. Here, a new calibration method based on multi-objective Bayesian optimization is developed to speed up the development of molecular dynamics force fields that are capable of predicting multiple properties accurately. This is achieved by reducing the number of simulation runs to generate the Pareto front with an efficient sequential sampling strategy. The methodology is demonstrated by generating a new coarse-grained force field for polycaprolactone, where the force field can predict mechanical properties and water diffusion in the polymer. 

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3. The UA?CG Workflow: High Performance Molecular Dynamics of Coarse-Grained Polymers

   https://doi.org/10.1109/PDP.2016.127  

   Ozog, David; Malony, Allen D.; Guenza, Marina (February 2016, Parallel, Distributed, and Network-Based Processing (DPD), 2016 24th Euromicro International Conference on)

   Our analytically based technique for coarse-graining (CG) polymer simulations dramatically improves spatial and temporal scaling while preserving thermodynamic quantities and bulk properties. The purpose of CG codes is to run more efficient molecular dynamics simulations, yet the research field generally lacks thorough analysis of how such codes scale with respect to full-atom representations. This paper conducts an in-depth performance study of highly realistic polymer melts on modern supercomputing systems. We also present a workflow that integrates our analytical solution for calculating CG forces with new high-performance techniques for mapping back and forth between the atomistic and CG descriptions in LAMMPS. The workflow benefits from the performance of CG, while maintaining full-atom accuracy. Our results show speedups up to 12x faster than atomistic simulations. 

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4. Analysis of the conformational properties of amine ligands at the gold/water interface with QM, MM and QM/MM simulations

   https://doi.org/10.1039/C7CP06709G  

   Liang, Dongyue; Hong, Jiewei; Fang, Dong; Bennett, Joseph W.; Mason, Sara E.; Hamers, Robert J.; Cui, Qiang (January 2018, Physical Chemistry Chemical Physics)

   We describe a strategy of integrating quantum mechanical (QM), hybrid quantum mechanical/molecular mechanical (QM/MM) and MM simulations to analyze the physical properties of a solid/water interface. This protocol involves using a correlated ab initio (CCSD(T)) method to first calibrate Density Functional Theory (DFT) as the QM approach, which is then used in QM/MM simulations to compute relevant free energy quantities at the solid/water interface using a mean-field approximation of Yang et al. that decouples QM and MM thermal fluctuations; gas-phase QM/MM and periodic DFT calculations are used to determine the proper QM size in the QM/MM simulations. Finally, the QM/MM free energy results are compared with those obtained from MM simulations to directly calibrate the force field model for the solid/water interface. This protocol is illustrated by examining the orientations of an alkyl amine ligand at the gold/water interface, since the ligand conformation is expected to impact the chemical properties ( e.g. , charge) of the solid surface. DFT/MM and MM simulations using the INTERFACE force field lead to consistent results, suggesting that the effective gold/ligand interactions can be adequately described by a van der Waals model, while electrostatic and induction effects are largely quenched by solvation. The observed differences among periodic DFT, QM/MM and MM simulations, nevertheless, suggest that explicitly including electronic polarization and potentially charge transfer in the MM model can be important to the quantitative accuracy. The strategy of integrating multiple computational methods to cross-validate each other for complex interfaces is applicable to many problems that involve both inorganic/metallic and organic/biomolecular components, such as functionalized nanoparticles. 

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5. Can a coarse-grained water model capture the key physical features of the hydrophobic effect?

   https://doi.org/10.1063/5.0176716  

   Ghosh, Kuntal; Loose, Timothy D; Voth, Gregory A (December 2023, The Journal of Chemical Physics)

   Coarse-grained (CG) molecular dynamics can be a powerful method for probing complex processes. However, most CG force fields use pairwise nonbonded interaction potentials sets, which can limit their ability to capture complex multi-body phenomena such as the hydrophobic effect. As the hydrophobic effect primarily manifests itself due to the nonpolar solute affecting the nearby hydrogen bonding network in water, capturing such effects using a simple one CG site or “bead” water model is a challenge. In this work, we systematically test the ability of CG one site water models for capturing critical features of the solvent environment around a hydrophobe as well as the potential of mean force (PMF) of neopentane association. We study two bottom-up models: a simple pairwise (SP) force-matched water model constructed using the multiscale coarse-graining method and the Bottom-Up Many-Body Projected Water (BUMPer) model, which has implicit three-body correlations. We also test the top-down monatomic (mW) and the Machine Learned mW (ML-mW) water models. The mW models perform well in capturing structural correlations but not the energetics of the PMF. BUMPer outperforms SP in capturing structural correlations and also gives an accurate PMF in contrast to the two mW models. Our study highlights the importance of including three-body interactions in CG water models, either explicitly or implicitly, while in general highlighting the applicability of bottom-up CG water models for studying hydrophobic effects in a quantitative fashion. This assertion comes with a caveat, however, regarding the accuracy of the enthalpy–entropy decomposition of the PMF of hydrophobe association. 

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