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Computational design of magnetic molecules and their environment using quantum chemistry, machine learning and multiscale simulations
Nature Reviews Chemistry ( IF 38.1 ) Pub Date : 2022-10-10 , DOI: 10.1038/s41570-022-00424-3
Alessandro Lunghi 1 , Stefano Sanvito 1
Affiliation  

Having served as a playground for fundamental studies on the physics of d and f electrons for almost a century, magnetic molecules are now becoming increasingly important for technological applications, such as magnetic resonance, data storage, spintronics and quantum information. All of these applications require the preservation and control of spins in time, an ability hampered by the interaction with the environment, namely with other spins, conduction electrons, molecular vibrations and electromagnetic fields. Thus, the design of a novel magnetic molecule with tailored properties is a formidable task, which does not only concern its electronic structures but also calls for a deep understanding of the interaction among all the degrees of freedom at play. This Review describes how state-of-the-art ab initio computational methods, combined with data-driven approaches to materials modelling, can be integrated into a fully multiscale strategy capable of defining design rules for magnetic molecules.



中文翻译:

使用量子化学、机器学习和多尺度模拟的磁性分子及其环境的计算设计

近一个世纪以来,磁性分子一直是 d 和 f 电子物理学基础研究的场所,现在在磁共振、数据存储、自旋电子学和量子信息等技术应用中变得越来越重要。所有这些应用都需要及时保存和控制自旋,这种能力受到与环境相互作用的阻碍,即与其他自旋、传导电子、分子振动和电磁场的相互作用。因此,设计具有定制特性的新型磁性分子是一项艰巨的任务,不仅涉及其电子结构,还需要深入了解所有自由度之间的相互作用。这篇评论描述了最先进的从头算计算方法,

更新日期:2022-10-11
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