Searching for the ground state of complex spin-ice systems using deep learning techniques.
Searching for the ground state of complex spin-ice systems using deep learning techniques.
复制标题
使用深度学习技术搜索复杂自旋冰系统的基态
DOI：
10.1038/s41598-022-19312-3
复制
发表时间：
2022-09-02
影响因子：
4.6
通讯作者：
Won, C.
中科院分区：
文献类型：
--
作者：
Kwon, H. Y.;Yoon, H. G.;Park, S. M.;Lee, D. B.;Shi, D.;Wu, Y. Z.;Choi, J. W.;Won, C.
寻找给定系统的基态是科学研究领域中最基本、最经典的问题之一。然而，当系统复杂而庞大时，它往往成为一个棘手的问题；在合理的计算资源下，基本上不可能找到全局能量最小状态。近年来，提出了一种基于深度学习技术的基态估计优化方法。我们将此方法应用于最复杂的自旋-冰体系之一，非周期Penrose P3模式。从结果中，我们发现了不同于先前已知的拓扑诱导涌现受挫自旋的新构型。此外，本研究还首次提出了一种尚未开发的Penrose P3自旋冰系统的候选基态。我们预计，深度学习技术的能力不仅将提高我们对人工自旋冰系统物理性质的理解，而且还将在需要计算方法进行优化的广泛科学研究领域取得重大进展。
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.
登录
查看更多内容
影响因子：
64.8
作者：
Jumper J;Evans R;Pritzel A;Green T;Figurnov M;Ronneberger O;Tunyasuvunakool K;Bates R;Žídek A;Potapenko A;Bridgland A;Meyer C;Kohl SAA;Ballard AJ;Cowie A;Romera-Paredes B;Nikolov S;Jain R;Adler J;Back T;Petersen S;Reiman D;Clancy E;Zielinski M;Steinegger M;Pacholska M;Berghammer T;Bodenstein S;Silver D;Vinyals O;Senior AW;Kavukcuoglu K;Kohli P;Hassabis D
通讯作者：
Hassabis D
DOI：
10.1002/advs.202004795
发表时间：
2021-06
期刊：
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
影响因子：
--
作者：
Kwon HY;Yoon HG;Park SM;Lee DB;Choi JW;Won C
通讯作者：
Won C
影响因子：
56.9
作者：
Carleo, Giuseppe;Troyer, Matthias
通讯作者：
Troyer, Matthias
影响因子：
3.7
作者：
Kwon, H. Y.;Kim, N. J.;Won, C.
通讯作者：
Won, C.
影响因子：
19.6
作者：
Mengotti, Elena;Heyderman, Laura J.;Braun, Hans-Benjamin
通讯作者：
Braun, Hans-Benjamin