Journal
JOURNAL OF PHYSICAL CHEMISTRY C
Volume 124, Issue 42, Pages 23158-23163Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcc.0c06673
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Funding
- NSERC
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We demonstrate the use of a regressive upscaling generative adversarial network (RUGAN) as an effective way to sample state space for hexagonal porous graphene sheets. The RUGAN can, after being trained on a set of small-scale examples, generate new, energetically relevant microstates (atomic configurations). The RUGAN can generate configurations across a continuum of total energy values and produce configurations at requested energy values. The microstates produced respect periodic boundary conditions, and importantly, the fully convolutional nature of the generator allows the generation of arbitrarily large microstates, after being trained on only a small-scale data set.
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