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

Journal of Cheminformatics

Toon Verstraelen¹  Michael Freitas Gustavo² 

[1] Center for Molecular Modeling, Ghent University, Ghent, Belgium;Center for Molecular Modeling, Ghent University, Ghent, Belgium;Software for Chemistry and Materials, De Boelelaan 1083, 1081 HV, Amsterdam, The Netherlands;

关键词: ReaxFF;    Global optimization;    Reparameterization;    Black-box optimization;    Python;    Parallel computation;   

DOI  :  10.1186/s13321-022-00581-z

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY