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 "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 ◽  
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

2020 ◽  
Author(s):  
Xiaoyong Cao ◽  
Pu Tian
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Molecular Modeling ◽  
Materials Science ◽  
Divide And Conquer ◽  
Machine Learning Techniques ◽  
Collective Variables ◽  
Spatial And Temporal Scales ◽  
Wide Range ◽  
State Models

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. To access interesting behavior of complex molecular systems in a wide range of spatial and temporal scales, the molecular modeling community has invested tremendous efforts 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. With introduction of machine learning techniques, many new developments, particularly those based on deep learning, have been implemented to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We recently developed the generalized solvation free energy theory , which suggests a third class of algorithm that facilitate molecular modeling through partially transferable in resolution "caching" of local free energy landscape. Connections and potential interactions among these three algorithmic directions are discussed. This brief review is on both the traditional development and the application of machine learning in molecular modeling from the perspective of "dividing and conquering" and "caching", with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms in this regard.

Divide-and-Conquer Based Adaptive Coarse Grained Simulation of RNA

2010 ◽  
Author(s):  
Mohammad Poursina ◽  
Kishor Bhalerao ◽  
Kurt Anderson
Keyword(s):  
Molecular Modeling ◽  
Degrees Of Freedom ◽  
Traditional Approach ◽  
Equations Of Motion ◽  
Critical Role ◽  
Divide And Conquer ◽  
Coarse Grained ◽  
Biomolecular Systems ◽  
Spatial And Temporal Scales ◽  
O 10

Molecular modeling has gained increasing importance in recent years for predicting important structural properties of large biomolecular systems such as RNA which play a critical role in various biological processes. Given the complexity of biopolymers and their interactions within living organisms, efficient and adaptive multi-scale modeling approaches are necessary if one is to reasonably perform computational studies of interest. These studies nominally involve multiple important physical phenomena occurring at different spatial and temporal scales. These systems are typically characterized by large number of degrees of freedom O(103) – O(107). The temporal domains range from sub-femto seconds (O(10−16)) associated with the small high frequency oscillations of individual tightly bonded atoms to milliseconds (O(10−3)) or greater for the larger scale conformational motion. The traditional approach for molecular modeling involved fully atomistic models which results in fully decoupled equations of motion. The problems with this approach are well documented in literature.

ROLE OF MOLECULAR MODELLING IN DRUG DESIGN

2016 ◽  
Vol 1 (02) ◽  
Author(s):  
M. Lakshmi Prabha ◽  
N. Pradeepa
Keyword(s):  
Molecular Modeling ◽  
Drug Design ◽  
Materials Science ◽  
Molecular System ◽  
Biological Molecules ◽  
Computational Techniques ◽  
Molecular Systems ◽  
Chemical Systems ◽  
Individual Unit

Molecular modeling includes all theoretical methods and computational techniques used to model or mimic the behavior of molecules. The techniques are used in the fields of computational chemistry, drug design, computational biology and materials science for studying molecular systems ranging from small chemical systems to large biological molecules and material assemblies. One can perform simplest calculation manually. But the computers are required to perform molecular modeling of any complex or reasonably sized system. Atomic level description of molecular system is the common feature of molecular modeling techniques. This may include treating atoms as the smallest individual unit (the Molecular mechanics approach), or explicitly modeling electrons of each atom (Parsons et al., 2005).

scholarly journals Bottom-Up Coarse-Grained Modeling of DNA

2021 ◽  
Vol 8 ◽  
Author(s):  
Tiedong Sun ◽  
Vishal Minhas ◽  
Nikolay Korolev ◽  
Alexander Mirzoev ◽  
Alexander P. Lyubartsev ◽  
...  
Keyword(s):  
Degrees Of Freedom ◽  
Deoxyribonucleic Acid ◽  
Persistence Length ◽  
Coarse Graining ◽  
Potential Of Mean Force ◽  
Coarse Grained ◽  
Bottom Up ◽  
Slow Dynamic ◽  
Molecular Systems ◽  
Fine Grained

Recent advances in methodology enable effective coarse-grained modeling of deoxyribonucleic acid (DNA) based on underlying atomistic force field simulations. The so-called bottom-up coarse-graining practice separates fast and slow dynamic processes in molecular systems by averaging out fast degrees of freedom represented by the underlying fine-grained model. The resulting effective potential of interaction includes the contribution from fast degrees of freedom effectively in the form of potential of mean force. The pair-wise additive potential is usually adopted to construct the coarse-grained Hamiltonian for its efficiency in a computer simulation. In this review, we present a few well-developed bottom-up coarse-graining methods, discussing their application in modeling DNA properties such as DNA flexibility (persistence length), conformation, “melting,” and DNA condensation.

Minimalist models for proteins: a comparative analysis

2010 ◽  
Vol 43 (3) ◽  
pp. 333-371 ◽  
Author(s):  
Valentina Tozzini
Keyword(s):  
Phase Diagram ◽  
Comparative Analysis ◽  
Amino Acid ◽  
Degrees Of Freedom ◽  
Predictive Power ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Molecular Systems ◽  
Modern Molecular Biology ◽  
Functional Forms

AbstractThe last decade has witnessed a renewed interest in the coarse-grained (CG) models for biopolymers, also stimulated by the needs of modern molecular biology, dealing with nano- to micro-sized bio-molecular systems and larger than microsecond timescale. This combination of size and timescale is, in fact, hard to access by atomic-based simulations. Coarse graining the system is a route to be followed to overcome these limits, but the ways of practically implementing it are many and different, making the landscape of CG models very vast and complex.In this paper, the CG models are reviewed and their features, applications and performances compared. This analysis, restricted to proteins, focuses on the minimalist models, namely those reducing at minimum the number of degrees of freedom without losing the possibility of explicitly describing the secondary structures. This class includes models using a single or a few interacting centers (beads) for each amino acid.From this analysis several issues emerge. The difficulty in building these models resides in the need for combining transferability/predictive power with the capability of accurately reproducing the structures. It is shown that these aspects could be optimized by accurately choosing the force field (FF) terms and functional forms, and combining different parameterization procedures. In addition, in spite of the variety of the minimalist models, regularities can be found in the parameters values and in FF terms. These are outlined and schematically presented with the aid of a generic phase diagram of the polypeptide in the parameter space and, hopefully, could serve as guidelines for the development of minimalist models incorporating the maximum possible level of predictive power and structural accuracy.

Outreach Opportunities Using the Instructional SEM at Iowa State University

1996 ◽  
Vol 54 ◽  
pp. 412-413
Author(s):  
L. S. Chumbley ◽  
M. Meyer ◽  
K. Fredrickson ◽  
F.C. Laabs
Keyword(s):  
Materials Science ◽  
State University ◽  
Digital Cameras ◽  
Audio Signals ◽  
Iowa State University ◽  
Cornell University ◽  
Sem Image ◽  
Local Schools ◽  
Materials Science And Engineering ◽  
Video And Audio

The development of a scanning electron microscope (SEM) suitable for instructional purposes has created a large number of outreach opportunities for the Materials Science and Engineering (MSE) Department at Iowa State University. Several collaborative efforts are presently underway with local schools and the Department of Curriculum and Instruction (C&I) at ISU to bring SEM technology into the classroom in a near live-time, interactive manner. The SEM laboratory is shown in Figure 1.Interactions between the laboratory and the classroom use inexpensive digital cameras and shareware called CU-SeeMe, Figure 2. Developed by Cornell University and available over the internet, CUSeeMe provides inexpensive video conferencing capabilities. The software allows video and audio signals from Quikcam™ cameras to be sent and received between computers. A reflector site has been established in the MSE department that allows eight different computers to be interconnected simultaneously. This arrangement allows us to demonstrate SEM principles in the classroom. An Apple Macintosh has been configured to allow the SEM image to be seen using CU-SeeMe.

Everything you've always wanted to know about what your students think they know but were afraid to ask.

2000 ◽  
Vol 632 ◽  
Author(s):  
Eric Werwa
Keyword(s):  
Materials Science ◽  
Science Museum ◽  
Science And Engineering ◽  
Museum Exhibit ◽  
Museum Visitors ◽  
The Public ◽  
Materials Science And Engineering ◽  
Properties Of Materials ◽  
Formal Science ◽  
Lay Public

ABSTRACTA review of the educational literature on naive concepts about principles of chemistry and physics and surveys of science museum visitors reveal that people of all ages have robust alternative notions about the nature of atoms, matter, and bonding that persist despite formal science education experiences. Some confusion arises from the profound differences in the way that scientists and the lay public use terms such as materials, metals, liquids, models, function, matter, and bonding. Many models that eloquently articulate arrangements of atoms and molecules to informed scientists are not widely understood by lay people and may promote naive notions among the public. Shifts from one type of atomic model to another and changes in size scales are particularly confusing to learners. People's abilities to describe and understand the properties of materials are largely based on tangible experiences, and much of what students learn in school does not help them interpret their encounters with materials and phenomena in everyday life. Identification of these challenges will help educators better convey the principles of materials science and engineering to students, and will be particularly beneficial in the design of the Materials MicroWorld traveling museum exhibit.

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