Development of a Physics-Informed Neural Framework, MEOWN, for Rapid Prediction of Muon Stopping Sites in Crystalline Materials, for understanding Quantum Magnet employing Muon Spectroscopy

• URL: http://arxiv.org/abs/2609.17063v1
• Date: Tue, 15 Sep 2026 12:08:11 GMT
• Title: Development of a Physics-Informed Neural Framework, MEOWN, for Rapid Prediction of Muon Stopping Sites in Crystalline Materials, for understanding Quantum Magnet employing Muon Spectroscopy
• Abstract summary: Muon-spin rotation/relaxation is one of the most powerful microscopic probes for understanding magnetic order, spin dynamics, superconductivity, and complex magnetic phases in emerging quantum materials.<n>Here, we develop MEOWN, a machine-learning-based, physics-informed computational framework that combines a P-UEP model with machine-learning-guided optimization and symmetry-driven relaxation to predict energetically favorable muon stopping sites in crystalline solids.<n> MEOWN explicitly incorporates electrostatic interactions, electronic screening, polarization effects, and zero-point motion into the learning workflow, providing physically interpretable predictions with substantially reduced computational cost
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Muon-spin rotation/relaxation is one of the most powerful microscopic probes for understanding magnetic order, spin dynamics, superconductivity, and complex magnetic phases in emerging quantum materials. Quantitative interpretation depends critically on the accurate identification of the muon stopping site, a problem traditionally addressed using density functional theory-based structural relaxation. However, conventional DFT approaches are computationally demanding, time-consuming, and require extensive calculations. Here, we develop MEOWN (Muon Engine for Optimized Weighted Networks), a machine-learning-based, physics-informed computational framework that combines a Polarizable Unperturbed Electrostatic Potential (P-UEP) model with machine-learning-guided optimization and symmetry-driven relaxation to predict energetically favorable muon stopping sites in crystalline solids. MEOWN explicitly incorporates electrostatic interactions, electronic screening, polarization effects, and zero-point motion into the learning workflow, providing physically interpretable predictions with substantially reduced computational cost while maintaining physical consistency and scientific rigor. To validate the framework, we investigated several well-established benchmark materials, including MnSi, CoF2, CaF2, LiF, and NaF. The predicted muon stopping sites and dipolar fields are in close agreement with previously reported DFT+mu results, demonstrating the reliability and transferability of the approach across chemically diverse systems. Notably, MEOWN predicts the equilibrium muon stopping site within a few minutes, offering a significant speedup over conventional DFT-based calculations. This manuscript presents the theoretical foundations, computational methodology, and validation of MEOWN as a general-purpose software for rapid and reliable muon stopping-site prediction in crystalline materials.

Related papers

• Efficient, precise DFT calculations of NMR shieldings: Revisiting the finite field approach [3.0404059399127656]
  We show that RI-accelerated finite magnetic field calculations can obtain DFT-level NMR shielding constants very precisely.<n>Results demonstrate that RI-accelerated finite magnetic field calculations can obtain DFT-level NMR shielding constants very precisely.
  arXiv  Detail & Related papers  (2026-08-25T15:01:01Z)
• Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory [45.322818939044645]
  We aim to learn wavefunctions simulated by time-dependent density functional theory (TDDFT)<n>In real-time TDDFT, the electronic wavefunctions of a molecule evolve over time in response to an external excitation.<n>We propose OrbEvo, which learns to evolve the full electronic wavefunction coefficients across time steps.
  arXiv  Detail & Related papers  (2026-03-03T20:34:05Z)
• Machine Learning-Integrated Hybrid Fluid-Kinetic Framework for Quantum Electrodynamic Laser Plasma Simulations [0.0]
  The research introduces a machine learning-based three-dimensional hybrid fluid-particle-in-cell (PIC) system.<n>The technique employs fluid approximations for stable areas but activates the PIC solver when SwitchNet directs it to unstable sections.<n>The model produces precise predictions with coefficient of determination (R2) values above 0.95 and mean squared errors below 10-4 for all field components.
  arXiv  Detail & Related papers  (2025-10-13T09:07:59Z)
• Accelerating Long-Term Molecular Dynamics with Physics-Informed Time-Series Forecasting [7.705860755153007]
  Molecular dynamics (MD) simulation is vital for understanding atomic-scale processes in materials science and biophysics.<n>Traditional density functional theory (DFT) methods are computationally expensive, which limits the feasibility of long-term simulations.<n>We propose a novel approach that formulates MD simulation as a time-series forecasting problem.
  arXiv  Detail & Related papers  (2025-09-16T02:00:52Z)
• Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems [11.085215676429858]
  We create a scalable model for Density Functional Theory calculations with physical accuracy.<n>We show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18%.
  arXiv  Detail & Related papers  (2025-02-26T15:36:25Z)
• Dissipation-induced Quantum Homogenization for Temporal Information Processing [44.99833362998488]
  Quantum reservoirs have great potential as they utilize the complex real-time dissipative dynamics of quantum systems for information processing and target time-series generation without precise control or fine-tuning of the Hamiltonian parameters.<n>We propose the disordered quantum homogenizer as an alternative platform, and prove it satisfies the necessary and sufficient conditions - stability and contractivity - of the reservoir dynamics.<n>The results indicate that the quantum homogenization protocol, physically implementable as either nuclear magnetic resonance ensemble or a photonic system, can potentially function as a reservoir computer.
  arXiv  Detail & Related papers  (2024-12-13T09:05:41Z)
• Physics-Enhanced TinyML for Real-Time Detection of Ground Magnetic Anomalies [0.0]
  Space weather phenomena like geomagnetic disturbances (GMDs) pose significant risks to critical technological infrastructure. This paper develops a physics-guided TinyML framework to address the above challenges. It integrates physics-based regularization at the stages of model training and compression, thereby augmenting the reliability of predictions.
  arXiv  Detail & Related papers  (2023-11-19T23:20:16Z)
• Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning [46.08497356503155]
  Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT. Here we propose M-OFDFT, an OFDFT approach capable of solving molecular systems using a deep learning functional model.
  arXiv  Detail & Related papers  (2023-09-28T16:33:36Z)
• Experimental validation of the Kibble-Zurek Mechanism on a Digital Quantum Computer [62.997667081978825]
  The Kibble-Zurek mechanism captures the essential physics of nonequilibrium quantum phase transitions with symmetry breaking. We experimentally tested the KZM for the simplest quantum case, a single qubit under the Landau-Zener evolution. We report on extensive IBM-Q experiments on individual qubits embedded in different circuit environments and topologies.
  arXiv  Detail & Related papers  (2022-08-01T18:00:02Z)
• Demonstration of electron-nuclear decoupling at a spin clock transition [54.088309058031705]
  Clock transitions protect molecular spin qubits from magnetic noise. linear coupling to nuclear degrees of freedom causes a modulation and decay of electronic coherence. An absence of quantum information leakage to the nuclear bath provides opportunities to characterize other decoherence sources.
  arXiv  Detail & Related papers  (2021-06-09T16:23:47Z)
• OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features [42.96944345045462]
  textscOrbNet is shown to outperform existing methods in terms of learning efficiency and transferability. For applications to datasets of drug-like molecules, textscOrbNet predicts energies within chemical accuracy of DFT at a computational cost that is thousand-fold or more reduced.
  arXiv  Detail & Related papers  (2020-07-15T22:38:41Z)

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.