Searching for the ground state of complex spin-ice systems using deep learning techniques.

ABSTRACT: Searching for the ground state of a given system is one of the most fundamental and classical questions in scientific research fields. However, when the system is complex and large, it often becomes an intractable problem; there is essentially no possibility of finding a global energy minimum state with reasonable computational resources. Recently, a novel method based on deep learning techniques was devised as an innovative optimization method to estimate the ground state. We apply this method to one of the most complicated spin-ice systems, aperiodic Penrose P3 patterns. From the results, we discover new configurations of topologically induced emergent frustrated spins, different from those previously known. Additionally, a candidate of the ground state for a still unexplored type of Pen

SUBMITTER: Kwon HY 

PROVIDER: S-EPMC9440018 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature