Overcoming Large Time- and Length-Scale Challenges in Molecular Modeling: A Review of Atomistic to Mesoscale Coarse-Graining Methods

2010 ◽  
pp. 15-26
Keyword(s):  
Molecular Modeling ◽  
Large Time ◽  
Coarse Graining ◽  
Length Scale ◽  
Scale Challenges

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

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 of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, 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 no transferability is available. 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 and 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.

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

2021 ◽  
Vol 22 (9) ◽  
pp. 5053
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
Materials Science And Engineering ◽  
State Models ◽  
Molecular Science

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.

scholarly journals Thermodynamics at Very Long Time and Space Scales

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1090 ◽  
Author(s):  
Bjarne Andresen ◽  
Christopher Essex
Keyword(s):  
Large Time ◽  
Additional Constraint ◽  
Time Averaging ◽  
Length Scale ◽  
Slow Time ◽  
The Past ◽  
New Concepts ◽  
Molecular Scale ◽  
Long Time ◽  
Order Of Magnitude

Any observation, and hence concept, is limited by the time and length scale of the observer and his instruments. Originally, we lived on a timescale of minutes and a length scale of meters, give or take an order of magnitude or two. Therefore, we devloped laboratory sized concepts, like volume, pressure, and temperature of continuous media. The past 150 years we managed to observe on the molecular scale and similarly nanoseconds timescale, leading to atomic physics that requires new concepts. In this paper, we are moving in the opposite direction, to extremely large time and length scales. We call this regime “slow time”. Here, we explore which laboratory concepts still apply in slow time and which new ones may emerge. E.g., we find that temperature no longer exists and that a new component of entropy emerges from long time averaging of other quantities. Just as finite-time thermodynamics developed from the small additional constraint of a finite process duration, here we add a small new condition, the very long timescale that results in a loss of temporal resolution, and again look for new structure.

Coarse-graining the input of education and R&D in China

2016 ◽  
Vol 27 (12) ◽  
pp. 1650151
Author(s):  
Zhuo-Ming Ren ◽  
Yixiu Kong
Keyword(s):  
Large Time ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Dynamical Process ◽  
Data Sets ◽  
Section Data ◽  
Economic Complexity ◽  
Complex Processes ◽  
Causal Direction ◽  
Large Time Scale

Coarse-grained analysis enhances our understanding of complex processes such as physical procedures, economic complexity. We collect the data sets from 31 regions of China in terms of the gross regional domestic product (GRDP) and the expense invested in Education and R&D between 1998 and 2013, then employ the coarse-grained method to analyze the causal direction according to the cross-section data with time-series information. Specifically, the empirical results suggest that the share of the GRDP invested in Education and R&D in large time scale reveals a dynamical process due to economic complexity, but limits around to base lines.

scholarly journals "Dividing and Conquering" and "Caching" in Molecular Modeling

2021 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Materials Science ◽  
Force Fields ◽  
Coarse Graining ◽  
Divide And Conquer ◽  
Collective Variables ◽  
Molecular Systems ◽  
State Models ◽  
Molecular Science

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 of important methodological advancements in more than half century of molecule modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science based on force fields parameterization by coarse graining, 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 no transferability is available. 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 and 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.

Biomimetic materials: An introduction

1992 ◽  
Vol 50 (2) ◽  
pp. 1020-1021 ◽  
Author(s):  
M. Sarikaya ◽  
J. T. Staley ◽  
I. A. Aksay
Keyword(s):  
Self Assembly ◽  
Structural Control ◽  
Hierarchical Structures ◽  
Ceramic Composites ◽  
Advanced Materials ◽  
Length Scale ◽  
Spider Webs ◽  
Biomimetic Materials ◽  
Synthetic Materials ◽  
All Ceramic

Biomimetics is an area of research in which the analysis of structures and functions of natural materials provide a source of inspiration for design and processing concepts for novel synthetic materials. Through biomimetics, it may be possible to establish structural control on a continuous length scale, resulting in superior structures able to withstand the requirements placed upon advanced materials. It is well recognized that biological systems efficiently produce complex and hierarchical structures on the molecular, micrometer, and macro scales with unique properties, and with greater structural control than is possible with synthetic materials. The dynamism of these systems allows the collection and transport of constituents; the nucleation, configuration, and growth of new structures by self-assembly; and the repair and replacement of old and damaged components. These materials include all-organic components such as spider webs and insect cuticles (Fig. 1); inorganic-organic composites, such as seashells (Fig. 2) and bones; all-ceramic composites, such as sea urchin teeth, spines, and other skeletal units (Fig. 3); and inorganic ultrafine magnetic and semiconducting particles produced by bacteria and algae, respectively (Fig. 4).

Facet Topography and Dislocation Structure as a Possible Source for Electrical Heterogeneity in [001] TILT Bicrystals of YBa2Cu3O7-δ

1996 ◽  
Vol 54 ◽  
pp. 338-339
Author(s):  
I-Fei Tsu ◽  
D.L. Kaiser ◽  
S.E. Babcock
Keyword(s):  
Grain Boundary ◽  
Critical Current Density ◽  
Critical Current ◽  
Current Density ◽  
Electromagnetic Properties ◽  
Length Scale ◽  
Density Values ◽  
Current Densities ◽  
Applied Fields ◽  
A Current

A current theme in the study of the critical current density behavior of YBa2Cu3O7-δ (YBCO) grain boundaries is that their electromagnetic properties are heterogeneous on various length scales ranging from 10s of microns to ˜ 1 Å. Recently, combined electromagnetic and TEM studies on four flux-grown bicrystals have demonstrated a direct correlation between the length scale of the boundaries’ saw-tooth facet configurations and the apparent length scale of the electrical heterogeneity. In that work, enhanced critical current densities are observed at applied fields where the facet period is commensurate with the spacing of the Abrikosov flux vortices which must be pinned if higher critical current density values are recorded. To understand the microstructural origin of the flux pinning, the grain boundary topography and grain boundary dislocation (GBD) network structure of [001] tilt YBCO bicrystals were studied by TEM and HRTEM.

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