Agentic multi-fidelity learning of quasiparticle and excitonic properties

• URL: http://arxiv.org/abs/2606.07836v1
• Date: Fri, 05 Jun 2026 20:55:23 GMT
• Title: Agentic multi-fidelity learning of quasiparticle and excitonic properties
• Abstract summary: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in low-dimensional nanomaterials.<n>We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers.
• Score: 0.5219568203653523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.

Related papers

• Toward Resilient Many-Body Formulations under Incomplete Correlation Models: A Dual-Space Variational Formulation [4.921068458547621]
  We introduce a dual-space nonorthogonal configuration-interaction (DS-NOCI) framework built around this principle.<n>With exact matrix elements, the enlarged dual space gives a ground-state energy no higher than either the reference-only NOCI or correlated-only quantum-NOCI spaces.
  arXiv  Detail & Related papers  (2026-09-03T18:50:32Z)
• Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems [41.99844472131922]
  We propose a data-driven framework to learn interaction kernels in multi-agent systems.<n>Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data.
  arXiv  Detail & Related papers  (2026-03-16T21:19:25Z)
• Equivariant Evidential Deep Learning for Interatomic Potentials [55.6997213490859]
  Uncertainty quantification is critical for assessing the reliability of machine learning interatomic potentials in molecular dynamics simulations.<n>Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance.<n>We propose textitEquivariant Evidential Deep Learning for Interatomic Potentials ($texte2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly.
  arXiv  Detail & Related papers  (2026-02-11T02:00:25Z)
• Quantum Hall Effect at 0.002T [46.680073344221626]
  We demonstrate a significant reduction in external inhomogeneity using a double-layer graphene architecture separated by an ultra-thin hexagonal boron nitride layer.<n>Shubnikov de-Haas oscillations emerge at magnetic fields below 1 mT, while integer quantum Hall features are observed at 0.002T.<n>These results demonstrate the platform's suitability for investigating strongly correlated electronic phases in graphene-based heterostructures.
  arXiv  Detail & Related papers  (2026-01-22T14:40:33Z)
• Operator Lanczos Approach enabling Neural Quantum States as Real-Frequency Impurity Solvers [0.0]
  We present a real-frequency impurity solver based on neural quantum states (NQS)<n>We leverage this ability to solve multi-orbital impurity problems using a systematically improvable Segmented Commutator Operator-Lanczos construction.<n>Our benchmarks on both the single-orbital Anderson model and the multi-orbital Hubbard-Kanamori impurity Hamiltonian reveal excellent ground-state precision and the capacity to accurately resolve zero temperature spectral functions and self-energies.
  arXiv  Detail & Related papers  (2025-12-09T14:10:22Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Homogenization with Guaranteed Bounds via Primal-Dual Physically Informed Neural Networks [0.6999740786886536]
  This paper introduces a dual formulation for the PINN framework to improve the reliability of the homogenization of thermo-conductive composites.<n>We compare standard PINNs applied to smoothed material approximations with variational PINNs (VPINNs) using both spectral and neural network-based test functions.<n>Our results indicate that while strong-form PINNs may outperform VPINNs in controlled settings, they are sensitive to material discontinuities and may fail without clear diagnostics.
  arXiv  Detail & Related papers  (2025-09-09T10:42:55Z)
• Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials [0.0]
  Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts.<n>Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions.<n>Our approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moir'e materials.
  arXiv  Detail & Related papers  (2025-03-19T17:14:02Z)
• Electron-Electron Interactions in Device Simulation via Non-equilibrium Green's Functions and the GW Approximation [71.63026504030766]
  electron-electron (e-e) interactions must be explicitly incorporated in quantum transport simulation.<n>This study is the first one reporting large-scale atomistic quantum transport simulations of nano-devices under non-equilibrium conditions.
  arXiv  Detail & Related papers  (2024-12-17T15:05:33Z)
• From Peptides to Nanostructures: A Euclidean Transformer for Fast and Stable Machine Learned Force Fields [5.013279299982324]
  We propose a transformer architecture called SO3krates that combines sparse equivariant representations with a self-attention mechanism. SO3krates achieves a unique combination of accuracy, stability, and speed that enables insightful analysis of quantum properties of matter on extended time and system size scales.
  arXiv  Detail & Related papers  (2023-09-21T09:22:05Z)
• A multiconfigurational study of the negatively charged nitrogen-vacancy center in diamond [55.58269472099399]
  Deep defects in wide band gap semiconductors have emerged as leading qubit candidates for realizing quantum sensing and information applications. Here we show that unlike single-particle treatments, the multiconfigurational quantum chemistry methods, traditionally reserved for atoms/molecules, accurately describe the many-body characteristics of the electronic states of these defect centers.
  arXiv  Detail & Related papers  (2020-08-24T01:49:54Z)

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.