Approximate Functionals in Hypercomplex Kohn-Sham Theory
- URL: http://arxiv.org/abs/2102.08790v2
- Date: Wed, 23 Feb 2022 17:30:44 GMT
- Title: Approximate Functionals in Hypercomplex Kohn-Sham Theory
- Authors: Neil Qiang Su
- Abstract summary: The recently developed hypercomplex Kohn-Sham (HCKS) theory shows great potential to overcome the static/strong correlation issue in density functional theory (DFT)
This work focuses on approximate functionals in HCKS, seeking to gain more insights into functional development from the comparison between Kohn-Sham (KS) DFT and HCKS.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The recently developed hypercomplex Kohn-Sham (HCKS) theory shows great
potential to overcome the static/strong correlation issue in density functional
theory (DFT), which highlights the necessity of further exploration of the HCKS
theory toward better handling many-electron problem. This work mainly focuses
on approximate functionals in HCKS, seeking to gain more insights into
functional development from the comparison between Kohn-Sham (KS) DFT and HCKS.
Unlike KS-DFT, HCKS can handle different correlation effects by resorting to a
set of auxiliary orbitals with dynamically varying fractional occupations.
These orbitals of hierarchical correlation (HCOs) thus contain distinct
electronic information for better considering the exchange-correlation effect
in HCKS. The test on the triplet-singlet gaps shows that HCKS has much better
performance as compared to KS-DFT in use of the same functionals, and the
systematic errors of semi-local functionals can be effectively reduced by
including appropriate amount of the HCO-dependent Hartree-Fock (HF) exchange.
In contrast, KS-DFT shows large systematic errors, which are hardly reduced by
the functionals tested in this work. Therefore, HCKS creates new channels to
address to the strong correlation issue, and further development of functionals
that depend on HCOs and their occupations is necessary for the treatment of
strongly correlated systems.
Related papers
- Learning Equivariant Non-Local Electron Density Functionals [51.721844709174206]
We introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant graph neural networks.
EG-XC combines semi-local functionals with a non-local feature density parametrized by an equivariant nuclei-centered point cloud representation.
We find EG-XC to accurately reconstruct gold-standard' CCSD(T) energies on MD17.
arXiv Detail & Related papers (2024-10-10T14:31:45Z) - Benchmark Computations of Nearly Degenerate Singlet and Triplet states of N-heterocyclic Chromophores : II. Density-based Methods [0.0]
A set of functionals with the least mean absolute error (MAE) is proposed for both the approaches, LR-TDDFT and $Delta$SCF.
We have based our findings on extensive studies of three cyclazine-based molecular templates.
The role of exact-exchange, spin-contamination and spin-polarization in the context of DFT comes to the forefront in our studies.
arXiv Detail & Related papers (2024-08-09T07:47:38Z) - NeuralSCF: Neural network self-consistent fields for density functional theory [1.7667864049272723]
Kohn-Sham density functional theory (KS-DFT) has found widespread application in accurate electronic structure calculations.
We propose a neural network self-consistent fields (NeuralSCF) framework that establishes the Kohn-Sham density map as a deep learning objective.
arXiv Detail & Related papers (2024-06-22T15:24:08Z) - Quantum-Enhanced Neural Exchange-Correlation Functionals [0.193482901474023]
KohnSham Density Functional Theory (KS-DFT) provides the exact ground state energy and electron density of a molecule, contingent on the asyet unknown universal exchange-correlation (XC) functional.
Recent research has demonstrated that neural networks can efficiently learn to represent approximations to that functional, offering accurate generalizations to molecules not present during the training process.
With the latest advancements in quantum-enhanced machine learning (ML), evidence is growing that Quantum Neural Network (QNN) models may offer advantages in ML applications.
arXiv Detail & Related papers (2024-04-22T15:07:57Z) - Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs [85.40198664108624]
We propose Codomain Attention Neural Operator (CoDA-NO) to solve multiphysics problems with PDEs.
CoDA-NO tokenizes functions along the codomain or channel space, enabling self-supervised learning or pretraining of multiple PDE systems.
We find CoDA-NO to outperform existing methods by over 36% on complex downstream tasks with limited data.
arXiv Detail & Related papers (2024-03-19T08:56:20Z) - TC-LIF: A Two-Compartment Spiking Neuron Model for Long-Term Sequential
Modelling [54.97005925277638]
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
It remains a challenging task for state-of-the-art spiking neural networks (SNNs) to establish long-term temporal dependency between distant cues.
We propose a novel biologically inspired Two-Compartment Leaky Integrate-and-Fire spiking neuron model, dubbed TC-LIF.
arXiv Detail & Related papers (2023-08-25T08:54:41Z) - IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint
Multi-Agent Trajectory Prediction [73.25645602768158]
IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling.
Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
arXiv Detail & Related papers (2023-03-01T15:16:56Z) - 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) - Generalizability of density functionals learned from differentiable
programming on weakly correlated spin-polarized systems [2.896251429985507]
Kohn-Sham regularizer (KSR) is a machine learning approach that optimize a physics-informed exchange-correlation functional.
We evaluate the generalizability of KSR by training on atomic systems and testing on molecules at equilibrium.
Our nonlocal functional outperforms any existing machine learning functionals by predicting the ground-state energies of the test systems with a mean absolute error of 2.7 milli-Hartrees.
arXiv Detail & Related papers (2021-10-28T02:03:04Z) - eQE 2.0: Subsystem DFT Beyond GGA Functionals [58.720142291102135]
subsystem-DFT (sDFT) can dramatically reduce the computational cost of large-scale electronic structure calculations.
The key ingredients of sDFT are the nonadditive kinetic energy and exchange-correlation functionals which dominate it's accuracy.
eQE 2.0 delivers excellent interaction energies compared to conventional Kohn-Sham DFT and CCSD(T)
arXiv Detail & Related papers (2021-03-12T22:26:36Z) - Unity of Kohn-Sham Density Functional Theory and Reduced Density Matrix
Functional Theory [0.0]
This work presents a theory to unify the two independent theoretical frameworks of Kohn-Sham (KS) density functional theory (DFT) and reduced density matrix functional theory (RDMFT)
The generalization of the KS orbitals to hypercomplex number systems leads to the hypercomplex KS (HCKS) theory, which extends the search space for the density in KS-DFT to a space that is equivalent to natural spin orbitals with fractional occupations in RDMFT.
arXiv Detail & Related papers (2021-01-31T06:39:14Z)
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.