Robust linear-scaling optimization of compact localized orbitals in
density functional theory
- URL: http://arxiv.org/abs/2004.05901v2
- Date: Thu, 30 Sep 2021 16:13:28 GMT
- Title: Robust linear-scaling optimization of compact localized orbitals in
density functional theory
- Authors: Yifei Shi, Jessica Karaguesian, Rustam Z. Khaliullin
- Abstract summary: Locality of compact one-electron orbitals expanded strictly in terms of local subsets of basis functions can be exploited in density functional theory.
We show that the slow and unstable optimization of compact orbitals originates from the nearly-invariant mixing of compact orbitals.
- Score: 4.232614032390373
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Locality of compact one-electron orbitals expanded strictly in terms of local
subsets of basis functions can be exploited in density functional theory (DFT)
to achieve linear growth of computation time with systems size, crucial in
large-scale simulations. However, despite advantages of compact orbitals the
development of practical orbital-based linear-scaling DFT methods has long been
hindered because a compact representation of the electronic ground state is
difficult to find in a variational optimization procedure. In this work, we
show that the slow and unstable optimization of compact orbitals originates
from the nearly-invariant mixing of compact orbitals that are mostly but not
completely localized within the same subsets of basis functions. We also
construct an approximate Hessian that can be used to identify the problematic
nearly-invariant modes and obviate the variational optimization along them
without introducing significant errors into the computed energies. This enables
us to create a linear-scaling DFT method with a low computational overhead that
is demonstrated to be efficient and accurate in fixed-nuclei calculations and
molecular dynamics simulations of semiconductors and insulators.
Related papers
- Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networks [60.3634789164648]
Energy-based learning algorithms, such as predictive coding (PC), have garnered significant attention in the machine learning community.
We rigorously analyze the stability, robustness, and convergence of PC through the lens of dynamical systems theory.
arXiv Detail & Related papers (2024-10-07T02:57:26Z) - Time-Reversal Symmetry in RDMFT and pCCD with Complex-Valued Orbitals [0.0]
We show that complex solutions lower the energy when non-dynamic electronic correlation effects are pronounced.
Specifically, we find that complex solutions lower the energy when non-dynamic electronic correlation effects are pronounced.
We present numerical examples to illustrate and discuss these instabilities and possible problems introduced by N-representability violations.
arXiv Detail & Related papers (2024-10-04T17:22:03Z) - Quantum algorithms for the variational optimization of correlated electronic states with stochastic reconfiguration and the linear method [0.0]
We present quantum algorithms for the variational optimization of wavefunctions correlated by products of unitary operators.
While an implementation on classical computing hardware would require exponentially growing compute cost, the cost (number of circuits and shots) of our quantum algorithms is in system size.
arXiv Detail & Related papers (2024-08-03T17:53:35Z) - Unveiling Intrinsic Many-Body Complexity by Compressing Single-Body Triviality [1.2289361708127877]
We show that the total orbital correlation actually reveals and quantifies the intrinsic complexity of the wavefunction.
An iterative scheme is proposed to optimize the orbitals.
The optimized orbitals enable the limited TCCSD ansatz to capture more non-trivial information.
arXiv Detail & Related papers (2024-02-26T18:59:08Z) - Orbital-Free Density Functional Theory with Continuous Normalizing Flows [54.710176363763296]
Orbital-free density functional theory (OF-DFT) provides an alternative approach for calculating the molecular electronic energy.
Our model successfully replicates the electronic density for a diverse range of chemical systems.
arXiv Detail & Related papers (2023-11-22T16:42:59Z) - D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT)
We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity.
In addition, we show that our approach enables us to explore more complex neural-based wave functions.
arXiv Detail & Related papers (2023-03-01T10:38:10Z) - A self-consistent field approach for the variational quantum
eigensolver: orbital optimization goes adaptive [52.77024349608834]
We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE)
This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
arXiv Detail & Related papers (2022-12-21T23:15:17Z) - Hybrid Physical-Neural ODEs for Fast N-body Simulations [0.22419496088582863]
We present a new scheme to compensate for the small-scales approximations resulting from Particle-Mesh schemes for cosmological N-body simulations.
We find that our approach outperforms PGD for the cross-correlation coefficients, and is more robust to changes in simulation settings.
arXiv Detail & Related papers (2022-07-12T13:06:06Z) - Optimization on manifolds: A symplectic approach [127.54402681305629]
We propose a dissipative extension of Dirac's theory of constrained Hamiltonian systems as a general framework for solving optimization problems.
Our class of (accelerated) algorithms are not only simple and efficient but also applicable to a broad range of contexts.
arXiv Detail & Related papers (2021-07-23T13:43:34Z) - Fast Gravitational Approach for Rigid Point Set Registration with
Ordinary Differential Equations [79.71184760864507]
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA)
In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field.
We show that the new method class has characteristics not found in previous alignment methods.
arXiv Detail & Related papers (2020-09-28T15:05:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.