Preserved in Portico This version is not peer-reviewed
"Dividing and Conquering" and "Caching" in Molecular Modeling
: Received: 2 December 2020 / Approved: 3 December 2020 / Online: 3 December 2020 (10:41:48 CET)
: Received: 29 December 2020 / Approved: 5 January 2021 / Online: 5 January 2021 (11:13:28 CET)
: Received: 4 March 2021 / Approved: 4 March 2021 / Online: 4 March 2021 (09:54:42 CET)
A peer-reviewed article of this Preprint also exists.
Journal reference: International Journal of Molecular Sciences 2021, 22, 22095053
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.
dividing and conquering; caching; coarse graining; enhanced sampling; generalized solvation free energy; molecular simulation; local free energy landscape
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