【大学频道】卡弗里理论科学研究所呈献 | 复旦大学张颖研究员

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题   目：Simultaneous attenuation of both self - lnteraction error and nondynamic correlation error in density functional theory

报告人：张颖

单   位：复旦大学

时   间：2019-05-31

地   点：中国科学院大学

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报告提纲

We present a spin-pair distinctive algorithm in the context of adiabatic-connection fluctuation-dissipation (ACFD) theorem, which enables to quantify the self-interaction error (SIE) and the nondynamic/strong correlation error (NCE) in the direct random-phase approximation (dRPA). Using this knowledge, we propose a spin-component scaled dRPA (scsRPA) correlation model with simultaneous attenuation of both the SIE and the NCE. Along with the exact exchange, scsRPA is shown to present a comprehensive improvement over dRPA, as well as the well-established PBE and PBE0 functionals, for bonding energies of pronounced multi-reference characters and transition-metal complexes of strongly correlated systems, while consistently provide an accurate description for reaction energies, reaction barriers, and non-covalent bond interactions of weakly correlated systems.

个人简介

Dr. Igor Ying Zhang obtained his PhD degree in Chemistry from Xiamen University, China (2010) and KTH Royal institute of technology, Sweden (2011). In 2012, he joined the Fritz Haber Institute (FHI) Berlin, Germany with a Max-Planck fellowship and was then promoted to be a group leader soon. In March 2018, he moved to Department of Chemistry, Fudan University as an awardee of “the Recruitment Program for Young Professionals”. Dr. Igor Ying Zhang’s research focuses on the development of advanced first-principle electronic-structure methods towards predictive accuracy for molecules and materials. He is authored and co-authored 40 peer-reviewed papers and one Springer book with over 2600 citations and an H-index of 22.

会议简介

会议名称：Frontiers in Density Functional Theory and Beyond - Advances and Challenges

会议地点：中国科学院大学

主办方：中国科学院大学卡弗里理论科学研究所

The Workshop intends to review recent advances in exchange-correlation functionals, GW, novel approaches, Van der Waals interactions, large-scale and multiscale developments, machine learning.

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