Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Ma, Zewei; Wang, Shu; Kim, Minhee; Liu, Kaibo; Chen, Chun‐Long; Pan, Wenxiao

doi:10.1039/d1sm00364j

Cited by 17 publications

(14 citation statements)

References 69 publications

(85 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With these issues in mind, recent studies have sought to continually improve models available for the hierarchical assembly of peptoid and protein building blocks. Where possible, it is convenient to borrow force field terms from existing releases, a prominent example for CG models being MARTINI which was developed for transferability.…”

Section: Methods For Computational Prediction Of Self-assembly Outcomesmentioning

confidence: 99%

“…Taken together, these studies highlight several pitfalls in model development: (i) a CG force field should be transferable to systems beyond what it was directly parametrized for because force field development is arduous and the need for reparameterizations restricts accessibility; (ii) it should be based on ab initio or atomistic force fields that are well-adapted for the molecules and physical observables of interest; (iii) it should typically employ enhanced sampling methods to properly sample the potentials of mean force (PMFs) of slowtransitioning degrees of freedom 415 because nonergodic sampling give rise to systematic errors in parametrization; and (iv) it should determine interaction potentials iteratively to selfconsistency among the various degrees of freedom within the model. 446,453,454 With these issues in mind, recent studies have sought to continually improve models available for the hierarchical assembly of peptoid 447 and protein 455 building blocks. Where possible, it is convenient to borrow force field terms from existing releases, a prominent example for CG models being MARTINI 456 which was developed for transferability.…”

Section: Theoretical Framework For Predicting Assemblymentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Shao

Prelesnik

et al. 2022

Chem. Rev.

Self Cite

View full text Add to dashboard Cite

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from highinformation content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

show abstract

Section: Methods For Computational Prediction Of Self-assembly Outcomesmentioning

confidence: 99%

Section: Theoretical Framework For Predicting Assemblymentioning

confidence: 99%

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Shao

Prelesnik

et al. 2022

Chem. Rev.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also, in the same context as the transferability of the conservative forces (i.e., the many-body CG PMF), thermodynamic transferability of dynamic properties should be addressed to impart a high fidelity CG model. This particular area has not been extensively explored at the current stage, yet several preliminary directions have provided potential directions: dynamical rescaling, , energy renormalization, − and transfer learning using ML techniques …”

Section: Dynamics Of the Coarse-grained Modelsmentioning

confidence: 99%

“…This particular area has not been extensively explored at the current stage, yet several preliminary directions have provided potential directions: dynamical rescaling, 366,367 energy renormalization, 402−406 and transfer learning using ML techniques. 407 On the other hand, understanding accelerated CG dynamics produced using Hamiltonian mechanics can lessen complications from frictional interactions by introducing ad hoc physical scaling principles, e.g., excess entropy scaling. In this regard, rigorous physical scaling principles beyond the hard sphere description for explaining the excess entropy scaling are a promising area for future research.…”

Section: -3c Excess Entropy Scalingmentioning

confidence: 99%

See 1 more Smart Citation

Bottom-up Coarse-Graining: Principles and Perspectives

Jin

Pak

Durumeric

et al. 2022

J. Chem. Theory Comput.

129

147

View full text Add to dashboard Cite

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

show abstract

Machine Learning in Soft Matter: From Simulations to Experiments

Zhang,

Gong,

Jiang

2024

Adv Funct Materials

View full text Add to dashboard Cite

Soft matter with diverse functionalities that are easily designable has fascinated tremendous research interests in the past several decades. Nevertheless, the inherent confluence of time and length scale ubiquitous in soft matter immensely complicates the elucidation of the structure–property relationship and thereby severely impedes the function exploration of soft materials. Recently, the emergent machine learning (ML) techniques open new paradigms in property prediction and molecular design of functional materials, due to their extraordinarily distinguished performance in the aspect of trend identity and pattern extraction from data, and objective optimization by accelerating the guided search in high‐dimensional spaces. This review exclusively focuses on the current state‐of‐the‐art progress in the development of ML techniques applied in the realms of soft matter, ranging from coarse‐grained simulations to theoretical prediction on the structural formation and macroscopic properties, as well as the optimization and algorithm‐aided design in experiments. Finally, an outlook on the challenges and opportunities for this rapidly evolving field is discussed.

show abstract

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

Abstract: The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on...

Cited by 17 publications

References 69 publications

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Bottom-up Coarse-Graining: Principles and Perspectives

Machine Learning in Soft Matter: From Simulations to Experiments

Contact Info

Product

Resources

About