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Interactive Molecular Model Assembly with 3D Printing

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"Dividing and Conquering" and "Caching" in Molecular Modeling.

Xiaoyong Cao¹, Pu Tian^1,2

¹School of Life Sciences, Jilin University, Changchun 130012, China.

International Journal of Molecular Sciences

|June 2, 2021

Summary

Molecular modeling advances through coarse-graining and enhanced sampling algorithms. A new local free energy landscape approach offers partially transferable caching for complex molecular systems.

Keywords:

Monte Carlo simulation coarse graining force fields generalized solvation free energy local free energy landscape local sampling many body interactions molecular dynamics simulation molecular modeling multiscale neural network sampling

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Area of Science:

Computational molecular science
Physics
Chemistry
Materials Science
Engineering

Background:

Molecular modeling is crucial across scientific disciplines, with algorithmic advancements driven by 'divide and conquer' and 'caching' principles.
Key methods include coarse-graining (reducing particle representation) and enhanced sampling (optimizing configurational space exploration).
Deep learning is increasingly applied to enhance these established algorithmic directions.

Purpose of the Study:

To introduce a novel framework for classical computational molecular science.
To present the local free energy landscape approach as a new algorithmic direction.
To explore the interplay between existing and novel molecular modeling algorithms.

Main Methods:

Development of the local free energy landscape approach.
Utilizing principles of 'divide and conquer' and 'caching' in algorithm design.
Analyzing the connections and differences among coarse-graining, enhanced sampling, and the new framework.

Main Results:

Demonstration of the local free energy landscape approach.
This framework facilitates molecular modeling via partially transferable resolution caching.
The approach focuses on local clusters of molecular degrees of freedom.

Conclusions:

The local free energy landscape approach represents a third class of algorithms in molecular modeling.
This framework offers partially transferable caching, enhancing efficiency and accuracy.
Further development is encouraged to create more elegant and reliable algorithms for complex molecular systems.