Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Bag, Saientan; Meinel, Melissa K.; Müller‐Plathe, Florian

doi:10.1021/acs.jctc.2c00898

Cited by 8 publications

(15 citation statements)

References 44 publications

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“…A known limitation of CG models is the acceleration in molecular mobility, which results in a mismatch between the dynamical properties at the atomistic and CG level of resolution. Bag et al 276 used deep neural networks to optimize the parameters of a traditional force field for 2,3,4-trimethylpentane in order to minimize such a mismatch, using the diffusion coefficient as a target property. The acceleration was reduced from a factor 7 to a factor 2 for this system.…”

Section: Open Challenges and Future Outlookmentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

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Section: Open Challenges and Future Outlookmentioning

confidence: 99%

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Ricci

Vergadou

2023

J. Phys. Chem. B

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“…Relative permittivity ( ε ) is another static property utilized here to further validate the performance of the MARTINI force field. Given the prevailing consensus regarding the issue of artificial acceleration in the coarse-grained models, 58–60 the emphasis shifted to the relative disparities between systems, specifically those with and without ATPs. The fluctuation of the dipole moment was employed here for the estimation of relative permittivity, 61,62 Here, V represents the volume of the bulk systems, k B is the Boltzmann constant, and T is the temperature.…”

Section: Resultsmentioning

confidence: 99%

Understanding and fine tuning the propensity of ATP-driven liquid–liquid phase separation with oligolysine

Zhu,

Wu,

Luo

2024

Phys. Chem. Chem. Phys.

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“…Equilibration and production runs were subsequently carried out at NPT conditions, with each run lasting 10 ns. For a comprehensive description of the simulation protocol, refer to our earlier work (Meinel et al 2 and Bag et al 16 ).…”

Section: Simulation Details and Simulated Systemsmentioning

confidence: 99%

Synthetic Force-Field Database for Training Machine Learning Models to Predict Mobility-Preserving Coarse-Grained Molecular-Simulation Potentials

Bag,

Meinel,

Müller-Plathe

2024

J. Chem. Theory Comput.

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Balancing accuracy and efficiency is a common problem in molecular simulation. This tradeoff is evident in coarse-grained molecular dynamics simulation, which prioritizes efficiency, and all-atom molecular simulation, which prioritizes accuracy. Despite continuous efforts, creating a coarse-grained model that accurately captures both the system’s structure and dynamics remains elusive. In this article, we present a data-driven approach for constructing coarse-grained models that aim to describe both the structure and dynamics of the system equally well. While the development of machine learning models is well-received in the scientific community, the significance of dataset creation for these models is often overlooked. However, data-driven approaches cannot progress without a robust dataset. To address this, we construct a database of synthetic coarse-grained potentials generated from unphysical all-atom models. A neural network is trained with the generated database to predict the coarse-grained potentials of real liquids. We evaluate their quality by calculating the combined loss of structural and dynamical accuracy upon coarse-graining. When we compare our machine learning-based coarse-grained potential with the one from iterative Boltzmann inversion, the machine learning prediction turns out better for all eight hydrocarbon liquids we studied. As all-atom surfaces turn more nonspherical, both ways of coarse-graining degrade. Still, the neural network outperforms iterative Boltzmann inversion in constructing good quality coarse-grained models for such cases. The synthetic database and the developed machine learning models are freely available to the community, and we believe that our approach will generate interest in efficiently deriving accurate coarse-grained models for liquids.

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Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach

Cited by 8 publications

References 44 publications

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Understanding and fine tuning the propensity of ATP-driven liquid–liquid phase separation with oligolysine

Synthetic Force-Field Database for Training Machine Learning Models to Predict Mobility-Preserving Coarse-Grained Molecular-Simulation Potentials

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