An accurate DFT-1/2 approach for shallow defect states: Efficient calculation of donor binding energies in silicon

• URL: http://arxiv.org/abs/2508.14738v2
• Date: Wed, 29 Oct 2025 10:17:16 GMT
• Title: An accurate DFT-1/2 approach for shallow defect states: Efficient calculation of donor binding energies in silicon
• Authors: Joshua Claes, Bart Partoens, Dirk Lamoen, Marcelo Marques, Lara K. Teles,
• Abstract summary: We present a simple, practical protocol for shallow donors based on the DFT-1/2 approximate quasiparticle correction.<n>This approach delivers reliable donor binding energies with minimal computational overhead.<n>It is applied to group-V donors in Si, Si:X (X= P, As, Sb, Bi), the method yields binding energies in close agreement with experiment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate prediction of shallow-donor electron binding energies is critical for device modeling, dopant activation, and donor-based quantum technologies. Traditional beyond-DFT approaches (e.g., hybrid functionals, GW) are prohibitively expensive for the large supercells needed to capture the extended, hydrogenic wavefunctions, while semi-local DFT underestimates band gaps and suffers from delocalization errors. We present a simple, practical protocol for shallow donors based on the DFT-1/2 approximate quasiparticle correction that maintains the computational cost of standard DFT and enables supercells up to thousands of atoms. This approach provides a straightforward and reproducible workflow that delivers reliable donor binding energies with minimal computational overhead. Applied to group-V donors in Si, Si:X (X= P, As, Sb, Bi), the method yields binding energies in close agreement with experiment. We found that, for Si:Bi, it is essential to include spin-orbit coupling to achieve near-experimental values with a difference of only $\sim$ 4 meV. For arsenic, the method yields excellent agreement with experiment, with a difference of only ~0.3 meV. For antimony, the results match experiment to within ~5 meV, and for phosphorus, the deviation is within ~8 meV. Beyond its high accuracy, DFT-1/2 offers a significant practical advantage, providing a straightforward, reproducible, and transferable workflow that is less demanding than hybrid functional approaches while remaining fully generalizable to other shallow impurities in semiconductors.

Related papers

• General linear correction method for DFT+X energy: application to U-M (M=Al, Ga, In) alloys under high pressure [0.6916304459667297]
  We propose a general linear correction method that eliminates the ambiguous energy contributions introduced by the model Hamiltonian in DFT+X approaches.<n>The method is validated within the framework of DFT+U, an important member of the DFT+X family.<n>It is then applied to important nuclear materials of uranium-based binaries U-M (M=Al, Ga, In) alloys.
  arXiv  Detail & Related papers  (2026-02-28T14:08:41Z)
• DiffusionNFT: Online Diffusion Reinforcement with Forward Process [99.94852379720153]
  Diffusion Negative-aware FineTuning (DiffusionNFT) is a new online RL paradigm that optimize diffusion models directly on the forward process via flow matching.<n>DiffusionNFT is up to $25times$ more efficient than FlowGRPO in head-to-head comparisons, while being CFG-free.
  arXiv  Detail & Related papers  (2025-09-19T16:09:33Z)
• Fast and Generalizable parameter-embedded Neural Operators for Lithium-Ion Battery Simulation [1.099532646524593]
  We benchmark three operator-learning surrogates for the Single Particle Model (SPM): Deep Operator Networks (DeepONets), Fourier Neural Operators (FNOs) and a newly proposed parameter-embedded Fourier Neural Operator (PE-FNO)<n>DeepONet accurately replicates constant-current behaviour but struggles with more dynamic loads. FNO maintains mesh invariance and keeps concentration errors below 1 %, with voltage mean-absolute errors under 1.7 mV across all load types. PE-FNO executes approximately 200 times faster than a 16-thread SPM solver.
  arXiv  Detail & Related papers  (2025-08-11T15:31:23Z)
• On Disentangled Training for Nonlinear Transform in Learned Image Compression [59.66885464492666]
  Learned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs.<n>Existing LIC methods overlook the slow convergence caused by compacting energy in learning nonlinear transforms.<n>We propose a linear auxiliary transform (AuxT) to disentangle energy compaction in training nonlinear transforms.
  arXiv  Detail & Related papers  (2025-01-23T15:32:06Z)
• 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 (GNNs)<n>By applying an equivariant GNN on this point cloud, we capture molecular-range interactions in a scalable and accurate manner.<n> EG-XC excels in data efficiency and molecular size extrapolation on QM9, matching force fields trained on 5 times more and larger molecules.
  arXiv  Detail & Related papers  (2024-10-10T14:31:45Z)
• Time-Dependent Density Functional Theory with the Orthogonal Projector Augmented Wave Method [0.6856896119187885]
  Bl"ochl linearly maps smooth pseudo wavefunctions to the highly oscillatory all-electron DFT orbitals. We take the first step to make OPAW viable for post-DFT methods by implementing it in real-time time-dependent (TD) DFT.
  arXiv  Detail & Related papers  (2023-12-18T18:15:36Z)
• 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)
• LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning [56.88751562302793]
  Low-rank adaption (LoRA) has emerged to fine-tune large language models (LLMs) LoRAPrune is a new framework that delivers an accurate structured pruned model in a highly memory-efficient manner. LoRAPrune achieves a reduction in perplexity by 4.81 on WikiText2 and 3.46 on PTB, while also decreasing memory usage by 52.6%.
  arXiv  Detail & Related papers  (2023-05-28T15:15:48Z)
• 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)
• Comparison of Density-Matrix Corrections to Density Functional Theory [0.0]
  Density functional theory (DFT) fails to describe systems with statically correlated electrons. We transform DFT into a one-electron reduced density matrix theory (1-RDMFT) We generalize the information density matrix functional theory (iDMFT)
  arXiv  Detail & Related papers  (2022-12-29T16:50:16Z)
• Simulating Spin-Orbit Coupling With Quasidegenerate N-Electron Valence Perturbation Theory [77.34726150561087]
  We present the first implementation of spin-orbit coupling effects in SO-QDNEVPT2. The accuracy of these methods is tested for the group 14 and 16 hydrides, 3d and 4d transition metal ions, and two actinide dioxides.
  arXiv  Detail & Related papers  (2022-11-11T20:03:37Z)
• Density Functional Theory Transformed into a One-electron Reduced Density Matrix Functional Theory for the Capture of Static Correlation [0.0]
  Density functional theory (DFT) fails to describe accurately the electronic structure of strongly correlated systems. We show that DFT can be transformed into a one-electron reduced-density-matrix (1-RDM) functional theory. Our approach yields substantial improvements over traditional DFT in the description of static correlation in chemical structures and processes.
  arXiv  Detail & Related papers  (2022-01-11T01:41:53Z)
• 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)
• Approximations based on density-matrix embedding theory for density-functional theories [0.0]
  We give a detailed review of the basics of density-matrix embedding theory (DMET) and show how it can be used to supplement other DFTs. We highlight how the mappings of DFTs can be used to identify uniquely defined auxiliary systems and auxiliary projections.
  arXiv  Detail & Related papers  (2021-03-02T21:10:51Z)
• An Efficient DFT Solver for Nanoscale Simulations and Beyond [0.0]
  We present an alternative orbital-free DFT solver that extends the applicability of DFT to system sizes beyond the nanoscale. OE-SCF is an iterative solver where the (typically computationally expensive) Pauli potential is treated as an external potential and updated after each iteration. We carried out the largest ab initio simulation for silicon-based materials to date using just a single CPU.
  arXiv  Detail & Related papers  (2020-10-14T20:09:29Z)
• Excitation Energies from Thermally-Assisted-Occupation Density Functional Theory: Theory and Computational Implementation [0.0]
  We develop time-dependent (TD) TAO-DFT, which is a time-dependent, linear-response theory for excited states. TDTAO-DFT also yields zero singlet-triplet gap in the dissociation limit, for the ground singlet ($11Sigma_g+$) and the first triplet state ($13Sigma_u+$)
  arXiv  Detail & Related papers  (2020-07-15T10:12:59Z)

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.