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Design of 2D skyrmionic metamaterials through controlled assembly
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作者 qichen xu Zhuanglin Shen +7 位作者 Alexander Edström I.P.Miranda Zhiwei Lu Anders Bergman Danny Thonig Wanjian Yin Olle Eriksson Anna Delin 《npj Computational Materials》 2025年第1期591-600,共10页
Despite extensive research on magnetic skyrmions and antiskyrmions,a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying,or even tailor-made,topologies.We address this chal... Despite extensive research on magnetic skyrmions and antiskyrmions,a significant challenge remains in crafting nontrivial high-order skyrmionic textures with varying,or even tailor-made,topologies.We address this challenge,by focusing on a construction pathway of skyrmionic metamaterials within a monolayer thin film and suggest several skyrmionic metamaterials that are surprisingly stable,i.e.,long-lived,due to a self-stabilization mechanism.This makes these new textures promising for applications.Central to our approach is the concept of’simulated controlled assembly’,in short,a protocol inspired by’click chemistry’that allows for positioning topological magnetic structures where one likes,and then allowing for energy minimization to elucidate the stability.Utilizing high-throughput atomistic-spin-dynamic simulations alongside state-of-the-art AI-driven tools,we have isolated skyrmions(topological charge Q=1),antiskyrmions(Q=−1),and skyrmionium(Q=0).These entities serve as foundational’skyrmionic building blocks’to form the here-reported intricate textures.In this work,two key contributions are introduced to the field of skyrmionic systems.First,we present a novel combination of atomistic spin dynamics simulations and controlled assembly protocols for the stabilization and investigation of new topological magnets.Second,using the aforementioned methods we report on the discovery of skyrmionic metamaterials. 展开更多
关键词 SKYRMIONS skyrmionic metamaterials monolayer thin film controlled assembly topological magnets magnetic skyrmions antiskyrmions atomistic spin dynamics simulations
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机器学习在材料设计方面的研究进展 被引量:12
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作者 孙中体 李珍珠 +3 位作者 程观剑 徐其琛 侯柱锋 尹万健 《科学通报》 EI CAS CSCD 北大核心 2019年第32期3270-3275,共6页
新材料的发现是推动现代科学发展与技术革新的源动力之一,是当前促进经济发展与解决环境问题的迫切需求.传统的材料研发基于试错法,效率低且成本高.大量实验与计算模拟产生的数据为新材料的研发提供了新契机.基于这些数据,机器学习最近... 新材料的发现是推动现代科学发展与技术革新的源动力之一,是当前促进经济发展与解决环境问题的迫切需求.传统的材料研发基于试错法,效率低且成本高.大量实验与计算模拟产生的数据为新材料的研发提供了新契机.基于这些数据,机器学习最近在材料性能预测、新材料的发现与设计等领域取得了很大进展.譬如基于材料项目(materials project)数据库对钙钛矿材料的统计分类、结合高通量计算对双钙钛矿卤化物材料稳定性的预测,以及金属间化合物电催化剂的设计与筛选等.除了基于隐式模型的预测,机器学习也可以用来发现具有物理可解释性的显式描述符,从而加速新材料的发现. 展开更多
关键词 机器学习 材料设计 能源转换 描述符
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