Kwon, Hee Young Yoon H. G. Park S. M. Lee D. B. Shi D. Wu Y. Z. Choi, Jun Woo Won C. 2024-01-12T02:37:12Z 2024-01-12T02:37:12Z 2022-11-22 2022-09 2045-2322 https://pubs.kist.re.kr/handle/201004/76014 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 Penrose P3 spin-ice system is first proposed through this study. We anticipate that the capabilities of the deep learning techniques will not only improve our understanding on the physical properties of artificial spin-ice systems, but also bring about significant advances in a wide range of scientific research fields requiring computational approaches for optimization. English Nature Publishing Group Searching for the ground state of complex spin-ice systems using deep learning techniques Article 10.1038/s41598-022-19312-3 1 Scientific Reports, v.12, no.1 Scientific Reports 12 1 Y scie scopus 000849436000094