Perspective on coarse-graining, cognitive load, and materials simulation

Jankowski, Eric; Ellyson, Neale; Fothergill, Jenny; Leibowitz, Mitchell H.; Miller, Evan; Alberts, M; Chesser, Samantha; Guevara, Jaime D.; Jones, Chris D.; Klopfenstein, Mia; Noneman, Kendra; Singleton, Rachel; Uriarte-Mendoza, Ramon A.; Thomas, Stephen; Estridge, Carla E.; Jones, Matthew L.

doi:10.1016/j.commatsci.2019.109129

Cited by 9 publications

(5 citation statements)

References 96 publications

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“…For biomolecular systems, a wide variety of coarse grained models have been developed [ 20 , 21 , 23 , 24 , 80 , 81 ]. Another important subject of CG methodology development is materials science [ 82 , 83 ]. Earlier definition of CG particles are rather ad hoc [ 20 ].…”

Section: DC and “Caching” In Traditional Molecular Modelingmentioning

confidence: 99%

“Dividing and Conquering” and “Caching” in Molecular Modeling

Cao

Tian

2021

IJMS

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Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.

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Section: DC and “Caching” In Traditional Molecular Modelingmentioning

confidence: 99%

“Dividing and Conquering” and “Caching” in Molecular Modeling

Cao

Tian

2021

IJMS

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“…With respect to simulation, there are a number of application areas, and this review is far from comprehensive. We refer the readers to a number of excellent review papers which have covered these applications in detail. ,,, In the context of self-assembly, ML methods are aimed at improving three areas: (1) force field development, (2) sampling limitations, and (3) property prediction. Specifically, in the case of assembly on complex materials (e.g., protein adsorption on solid surfaces), the availability of validated force fields still limits the application of MD to a small number of material categories.…”

Section: Statistical Learning and Artificial Intelligencementioning

confidence: 99%

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

2020

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Molecular self-assembly is a powerful tool in materials design, wherein noncovalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable, and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing selfassembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physicsbased simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assemblybiomineralizing peptides, hierarchical peptoids, and large protein assembliesand will aid in the development of a new synthesis science for achieving precise molecular control in materials design.

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“…Molecular dynamics (MD) simulation can provide a nanoscale view of the donor–acceptor interface. Huang and others have studied semiconducting polymer morphologies using all-atom and coarse-grained MD simulations. − However, all-atom simulations of large, stiff, and slow relaxing semiconducting polymers are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Fast, Faithful Simulations of Donor–Acceptor Interface Morphology

Agarwala

Gomez

Milner

2022

J. Chem. Theory Comput.

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The local structure of conjugated polymers governs key optoelectronic properties, such as charge conduction and photogeneration at donor–acceptor interfaces. Because conjugated polymers are large, stiff, and relax slowly, all-atom molecular dynamics simulations are computationally expensive. Here, we describe a coarse-graining method that exploits the stiffness of constituent aromatic moieties by representing each moiety as rigidly bonded clusters of atoms wherein virtual sites replace several atoms. This approach significantly reduces the degrees of freedom while faithfully representing the shape and interactions of the moieties, resulting in 10 times faster simulations than all-atom simulations. Simulation of a donor polymer (P3HT) and a non-fullerene acceptor (O-IDTBR) validates the coarse-graining method by comparing structural properties from experiments, such as the density and persistence length. The fast simulation produces equilibrated systems with realistic morphologies. The simulation results of an equimolar mixture of P3HT, with a molecular weight of 1332 g mol–1, and an O-IDTBR mixture suggest that the interface width must be larger than 7 nm. Also, we investigate the effect of slow cooling on morphologies, particularly the number of close contacts that facilitates carrier transport. Slow cooling increases close contacts, and the effect is more pronounced in crystal-forming P3HT than in O-IDTBR, where bulky side-groups hinder crystal formation.

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Perspective on coarse-graining, cognitive load, and materials simulation

Cited by 9 publications

References 96 publications

“Dividing and Conquering” and “Caching” in Molecular Modeling

“Dividing and Conquering” and “Caching” in Molecular Modeling

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

Fast, Faithful Simulations of Donor–Acceptor Interface Morphology

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