Literature DB >> 31441083

GPU-Accelerated Large-Scale Excited-State Simulation Based on Divide-and-Conquer Time-Dependent Density-Functional Tight-Binding.

Takeshi Yoshikawa1, Nana Komoto2, Yoshifumi Nishimura1, Hiromi Nakai1,2,3.   

Abstract

The present study implemented the divide-and-conquer time-dependent density-functional tight-binding (DC-TDDFTB) code on a graphical processing unit (GPU). The DC method, which is a linear-scaling scheme, divides a total system into several fragments. By separately solving local equations in individual fragments, the DC method could reduce slow central processing unit (CPU)-GPU memory access, as well as computational cost, and avoid shortfalls of GPU memory. Numerical applications confirmed that the present code on GPU significantly accelerated the TDDFTB calculations, while maintaining accuracy. Furthermore, the DC-TDDFTB simulation of 2-acetylindan-1,3-dione displays excited-state intramolecular proton transfer and provides reasonable absorption and fluorescence energies with the corresponding experimental values.
© 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  divide-and-conquer method; excited-state theory; graphical processor unit; linear scaling; time-dependent density-functional tight-binding method

Year:  2019        PMID: 31441083     DOI: 10.1002/jcc.26053

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  3 in total

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Authors:  David B Williams-Young; Wibe A de Jong; Hubertus J J van Dam; Chao Yang
Journal:  Front Chem       Date:  2020-12-10       Impact factor: 5.221

2.  Potential of phytocompounds from Brassica oleracea targeting S2-domain of SARS-CoV-2 spike glycoproteins: Structural and molecular insights.

Authors:  Sandra Jose; Megha Gupta; Urvashi Sharma; Jorge Quintero-Saumeth; Manish Dwivedi
Journal:  J Mol Struct       Date:  2022-01-08       Impact factor: 3.196

Review 3.  Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning.

Authors:  Wei Li; Haibo Ma; Shuhua Li; Jing Ma
Journal:  Chem Sci       Date:  2021-11-08       Impact factor: 9.825

  3 in total

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