PySCF: the Python‐based simulations of chemistry framework
- Qiming Sun, Timothy C. Berkelbach, G. Chan
- Computer Science
- 2018
The capabilities and design philosophy of the current version of the PySCF package are document, which is as efficient as the best existing C or Fortran‐based quantum chemistry programs.
Density matrix embedding: a simple alternative to dynamical mean-field theory.
Frequency independence and the minimal bath make DMET a computationally simple and efficient method and compared to benchmark data, total energies, correlation functions, and metal-insulator transitions are well reproduced, at a tiny computational cost.
Advances in molecular quantum chemistry contained in the Q-Chem 4 program package
- Y. Shao, Zhengting Gan, M. Head‐Gordon
- ChemistryMolecular Physics
- 3 September 2014
Detailed benchmarks of the comparative accuracy of modern density functionals for bonded and non-bonded interactions, tests of attenuated second order Møller–Plesset methods for intermolecular interactions, and tests of the accuracy of implicit solvation models are provided.
Stripe order in the underdoped region of the two-dimensional Hubbard model
- B. Zheng, Chia-Min Chung, G. Chan
- PhysicsScience
- 31 December 2016
A stripe order that has a highly compressible wavelength on an energy scale of a few kelvin, with wavelength fluctuations coupled to pairing order is found, demonstrating the power of modern numerical methods to solve microscopic models, even in challenging settings.
Efficient tree tensor network states (TTNS) for quantum chemistry: generalizations of the density matrix renormalization group algorithm.
- N. Nakatani, G. Chan
- Computer Science, PhysicsJournal of Chemical Physics
- 10 February 2013
The concept of half-renormalization is introduced which greatly improves the efficiency of the calculations and demonstrates the strengths and weaknesses of tree tensor network states versus matrix product states.
The density matrix renormalization group in quantum chemistry.
- G. Chan, Sandeep Sharma
- PhysicsAnnual review of physical chemistry (Print)
- 31 March 2011
A pedagogical overview of the basic challenges of strong correlation, how the density matrix renormalization group works, a survey of its existing applications to molecular problems, and some thoughts on the future of the method are provided.
Highly correlated calculations with a polynomial cost algorithm: A study of the density matrix renormalization group
- G. Chan, M. Head‐Gordon
- Physics
- 5 March 2002
We study the recently developed Density Matrix Renormalization Group (DMRG) algorithm in the context of quantum chemistry. In contrast to traditional approaches, this algorithm is believed to yield…
A Practical Guide to Density Matrix Embedding Theory in Quantum Chemistry.
- S. Wouters, C. Jiménez-Hoyos, Qiming Sun, G. Chan
- Physics, Computer ScienceJournal of Chemical Theory and Computation
- 28 March 2016
This work gives a practically oriented and explicit description of the numerical and theoretical formulation of DMET, and describes in detail how to perform self-consistent DMET optimizations.
Solutions of the Two-Dimensional Hubbard Model: Benchmarks and Results from a Wide Range of Numerical Algorithms
- J. LeBlanc, A. Antipov, E. Gull
- Physics
- 9 May 2015
Numerical results for ground-state and excited-state properties (energies, double occupancies, and Matsubara-axis self-energies) of the single-orbital Hubbard model on a two-dimensional square…
Density Matrix Embedding: A Strong-Coupling Quantum Embedding Theory.
It is found that DMET correctly describes the notoriously difficult symmetric dissociation of a 4 × 3 hydrogen atom grid, even when the treated fragments are as small as single hydrogen atoms.
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