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Accelerating Linear-Response Time-Dependent Hybrid Density Functional Theory with Low-Rank Decomposition Techniques

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We developed an efficient low-rank method for hybrid-LR-TDDFT calculations. This approach accelerates the study of excitonic properties in materials like MoS2, reducing computational costs significantly.

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Area of Science:

  • Computational materials science
  • Quantum chemistry
  • Condensed matter physics

Background:

  • Linear-response time-dependent density functional theory (LR-TDDFT) is crucial for studying electronic excitations.
  • Hybrid functionals in TDDFT offer improved accuracy but are computationally expensive.
  • Efficient methods are needed to study large systems and complex electronic properties.

Purpose of the Study:

  • To present an efficient low-rank implementation of LR-TDDFT for hybrid functionals (hybrid-LR-TDDFT).
  • To introduce adaptively compressed exchange (ACE) operator and natural transition orbitals (NTOs) for computational acceleration.
  • To enable the study of excitonic properties in large material systems.

Main Methods:

  • Implementation of a low-rank approximation for the nonlocal exchange operator in hybrid-LR-TDDFT.
  • Utilizing the adaptively compressed exchange (ACE) operator.
  • Incorporating natural transition orbitals (NTOs) with a cutoff parameter for further acceleration.

Main Results:

  • The ACE approximation significantly reduces computational cost without sacrificing accuracy.
  • NTO approximation further accelerates calculations, enabling efficient studies.
  • Successfully studied excitonic properties of 2D MoS2 (216 atoms) on a single GPU.

Conclusions:

  • The developed hybrid-LR-TDDFT method is computationally efficient and accurate.
  • This approach facilitates the investigation of excitonic properties in large, complex systems.
  • The method opens new possibilities for materials science research using hybrid functionals.