Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 16, Issue 3, Pages 1448-1455Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.9b00883
Keywords
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Funding
- National Science Foundation Graduate Fellowship Program (NSF-GRFP)
- University of Chicago Research Computing Center
- Department of Energy, Basic Energy Sciences, Materials Science and Engineering Division, through the Midwest Integrated Center for Computational Materials (MICCoM)
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An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.
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