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1. 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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2. Accessing the electronic structure of liquid crystalline semiconductors with bottom-up electronic coarse-graining

   https://doi.org/10.1039/D3SC06749A  

   Wang, Chun-I; Maier, J Charlie; Jackson, Nicholas E (June 2024, Chemical Science)

   Understanding the relationship between multiscale morphology and electronic structure is a grand challenge for semiconducting soft materials. Computational studies aimed at characterizing these multiscale relationships require the complex integration of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, the integration and scalability of these methods pose substantial computational challenges that limit their application to the requisite length scales of soft material morphologies. Here, we demonstrate the bottom-up electronic coarse-graining (ECG) of morphology-dependent electronic structure in the liquid-crystal-forming semiconductor, 2-(4-methoxyphenyl)-7-octyl-benzothienobenzothiophene (BTBT). ECG is applied to construct density functional theory (DFT)-accurate valence band Hamiltonians of the isotropic and smectic liquid crystal (LC) phases using only the CG representation of BTBT. By bypassing the atomistic resolution and its prohibitive computational costs, ECG enables the first calculations of the morphology dependence of the electronic structure of charge carriers across LC phases at the ~20 nm length scale, with robust statistical sampling. kinetic Monte Carlo (kMC) simulations reveal a strong morphology dependence on zero-field charge mobility among different LC phases as well as the presence of two-molecule charge carriers that act as traps and hinder charge transport. We leverage these results to further evaluate the feasibility of developing truly mesoscopic, field-based ECG models in future works. The fully CG approach to electronic property predictions in LC semiconductors opens a new computational direction for designing electronic processes in soft materials at their characteristic length scales. 

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3. Coarse-grained force-field for large scale molecular dynamics simulations of polyacrylamide and polyacrylamide-gels based on quantum mechanics

   https://doi.org/10.1039/d0cp05767c  

   Zheng, Mei; Jaramillo-Botero, Andres; Ju, Xue-hai; Goddard, William A. (May 2021, Physical Chemistry Chemical Physics)

   null (Ed.)

   We developed a new coarse-grained (CG) molecular dynamics force field for polyacrylamide (PAM) polymer based on fitting to the quantum mechanics (QM) equation of state (EOS). In this method, all nonbond interactions between representative beads are parameterized using a series of QM-EOS, which significantly improves the accuracy in comparison to common CG methods derived from atomistic molecular dynamics. This CG force-field has both higher accuracy and improved computational efficiency with respect to the OPLS atomistic force field. The nonbond components of the EOS were obtained from cold-compression curves on PAM crystals with rigid chains, while the covalent terms that contribute to the EOS were obtained using relaxed chains. For describing PAM gels we developed water–PAM interaction parameters using the same method. We demonstrate that the new CG-PAM force field reproduces the EOS of PAM crystals, isolated PAM chains, and water–PAM systems, while successfully predicting such experimental quantities as density, specific heat capacity, thermal conductivity and melting point. 

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4. 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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5. Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems

   https://doi.org/10.1063/5.0146812  

   Durumeric, Aleksander_E P; Voth, Gregory A (June 2023, The Journal of Chemical Physics)

   Bottom-up coarse-grained (CG) molecular dynamics models are parameterized using complex effective Hamiltonians. These models are typically optimized to approximate high dimensional data from atomistic simulations. However, human validation of these models is often limited to low dimensional statistics that do not necessarily differentiate between the CG model and said atomistic simulations. We propose that classification can be used to variationally estimate high dimensional error and that explainable machine learning can help convey this information to scientists. This approach is demonstrated using Shapley additive explanations and two CG protein models. This framework may also be valuable for ascertaining whether allosteric effects at the atomistic level are accurately propagated to a CG model. 

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