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Sparse tensor framework for implementation of general local correlation methods.

Daniel Kats1, Frederick R Manby1

  • 1Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, United Kingdom.

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Summary
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Automating local coupled-cluster methods using sparse tensors significantly enhances computational efficiency. This breakthrough enables accurate chemical property calculations for larger systems, overcoming previous limitations.

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Area of Science:

  • Computational chemistry
  • Quantum chemistry
  • Materials science

Background:

  • Coupled-cluster (CC) methods provide high accuracy for chemical properties.
  • Steep computational cost scaling limits CC methods for large systems.
  • Local approximations address scaling but increase complexity.

Purpose of the Study:

  • To develop an automated implementation scheme for local coupled-cluster (LCC) methods.
  • To overcome the complexity of LCC implementations.
  • To demonstrate the efficacy of the automated scheme for various CC models.

Main Methods:

  • An automated implementation scheme for LCC methods was developed.
  • The scheme utilizes an interpreter and a sparse tensor representation.
  • This approach simplifies the integration of LCC methods.

Main Results:

  • The automated scheme was successfully implemented.
  • A wide range of singles-and-doubles-based coupled-cluster schemes were tested.
  • The approach demonstrated its efficacy in handling complex LCC calculations.

Conclusions:

  • Automated LCC methods are feasible and effective.
  • The use of sparse tensors and interpreters simplifies LCC implementation.
  • This work paves the way for more widespread application of accurate CC methods.