Advances in modeling attosecond electron dynamics in molecular photoionization

IF 16.8 2区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Wiley Interdisciplinary Reviews: Computational Molecular Science Pub Date : 2023-05-23 DOI:10.1002/wcms.1673
Marco Ruberti, Vitali Averbukh
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引用次数: 2

Abstract

The dramatic progress of experimental attosecond science has called for the development of new theoretical and computational tools capable of accurately model the correlated electron dynamics triggered by attosecond molecular photoionization. We describe the nature and the main outcome of this development, with particular focus on the B-spline ADC and RCS-ADC ab initio methods.

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分子光电离中阿秒电子动力学建模研究进展
实验阿秒科学的巨大进步要求开发新的理论和计算工具,能够准确地模拟阿秒分子光电离引发的相关电子动力学。我们描述了这一发展的性质和主要结果,特别关注B样条ADC和RCS-ADC从头算方法。本文分类如下:
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来源期刊
Wiley Interdisciplinary Reviews: Computational Molecular Science
Wiley Interdisciplinary Reviews: Computational Molecular Science CHEMISTRY, MULTIDISCIPLINARY-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
28.90
自引率
1.80%
发文量
52
审稿时长
6-12 weeks
期刊介绍: Computational molecular sciences harness the power of rigorous chemical and physical theories, employing computer-based modeling, specialized hardware, software development, algorithm design, and database management to explore and illuminate every facet of molecular sciences. These interdisciplinary approaches form a bridge between chemistry, biology, and materials sciences, establishing connections with adjacent application-driven fields in both chemistry and biology. WIREs Computational Molecular Science stands as a platform to comprehensively review and spotlight research from these dynamic and interconnected fields.
期刊最新文献
Issue Information Embedded Many-Body Green's Function Methods for Electronic Excitations in Complex Molecular Systems ROBERT: Bridging the Gap Between Machine Learning and Chemistry Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy Computational design of energy-related materials: From first-principles calculations to machine learning
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