GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations

In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimiz...

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Vydáno v:Journal of cheminformatics Ročník 14; číslo 1; s. 7 - 29
Hlavní autoři: Freitas Gustavo, Michael, Verstraelen, Toon
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 16.02.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Black-box optimization
Chemistry
Chemistry and Materials Science
Computational Biology/Bioinformatics
Computer Applications in Chemistry
Documentation and Information in Chemistry
Global optimization
Hybridization
Minima
Optimization
Parallel computation
Python
ReaxFF
Reparameterization
Software
Theoretical and Computational Chemistry
Workflow
ReaxFF
Global optimization
Parallel computation
Reparameterization
Black-box optimization
Python
ISSN:1758-2946, 1758-2946
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Shrnutí:In this work we explore the properties which make many real-life global optimization problems extremely difficult to handle, and some of the common techniques used in literature to address them. We then introduce a general optimization management tool called GloMPO (Globally Managed Parallel Optimization) to help address some of the challenges faced by practitioners. GloMPO manages and shares information between traditional optimization algorithms run in parallel. We hope that GloMPO will be a flexible framework which allows for customization and hybridization of various optimization ideas, while also providing a substitute for human interventions and decisions which are a common feature of optimization processes of hard problems. GloMPO is shown to produce lower minima than traditional optimization approaches on global optimization test functions, the Lennard-Jones cluster problem, and ReaxFF reparameterizations. The novel feature of forced optimizer termination was shown to find better minima than normal optimization. GloMPO is also shown to provide qualitative benefits such a identifying degenerate minima, and providing a standardized interface and workflow manager.
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ISSN:1758-2946
1758-2946
DOI:10.1186/s13321-022-00581-z