Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Factorization in Molecular Modeling and Belief Propagation Algorithms

Version 1 : Received: 7 October 2021 / Approved: 7 October 2021 / Online: 7 October 2021 (10:31:52 CEST)

How to cite: Tian, P. Factorization in Molecular Modeling and Belief Propagation Algorithms. Preprints 2021, 2021100113 (doi: 10.20944/preprints202110.0113.v1). Tian, P. Factorization in Molecular Modeling and Belief Propagation Algorithms. Preprints 2021, 2021100113 (doi: 10.20944/preprints202110.0113.v1).

Abstract

Factorization reduces computational complexity and is therefore an important tool in statistical machine learning of high dimensional systems. Conventional molecular modeling, including molecular dynamics and Monte Carlo simulations of molecular systems, is a large research field based on approximate factorization of molecular interactions. Recently, the local distribution theory was proposed to factorize global joint distribution of a given molecular system into trainable local distributions. Belief propagation algorithms are a family of exact factorization algorithms for trees and are extended to approximate loopy belief propagation algorithms for graphs with loops. Despite the fact that factorization of probability distribution is their common foundation, computational research in molecular systems and machine learning studies utilizing belief propagation algorithms have been carried out independently with respective track of algorithm development. The connection and differences among these factorization algorithms are briefly presented in this perspective, with the hope to intrigue further development in factorization algorithms for physical modeling of complex molecular systems.

Keywords

Factorization; molecular modeling; belief propagation; sum-product; local distribution theory

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.