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Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy
Authors:
Abhijatmedhi Chotrattanapituk,
Ryotaro Okabe,
Eunbi Rha,
Mariya Al-Hinai,
Eugene Jiang,
Daniel Pajerowski,
Yongqiang Cheng,
Joshua J. Turner,
Mingda Li
Abstract:
Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an…
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Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials.
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Submitted 15 May, 2026;
originally announced May 2026.
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Lindbladian Learning with Neural Differential Equations
Authors:
Timothy Heightman,
Roman Aseguinolaza Gallo,
Edward Jiang,
JRM Saavedra,
Antonio Acín,
Marcin Płodzień
Abstract:
Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the system is open, this task becomes considerably harder than in the purely unitary case, because coherent and dissipative mechanisms can produce similar measurement statistics and long-time data can be insensitive to coheren…
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Inferring the dynamical generator of a many-body quantum system from measurement data is essential for the verification, calibration, and control of quantum processors. When the system is open, this task becomes considerably harder than in the purely unitary case, because coherent and dissipative mechanisms can produce similar measurement statistics and long-time data can be insensitive to coherent couplings. Here we tackle this so-called Lindbladian learning problem of open-system characterisation with maximum-likelihood on Pauli measurements at multiple experimentally friendly \emph{transient} times, exploiting the richer information content of transient dynamics. To navigate the resulting non-convex likelihood loss-landscape, we augment the physical model neural differential-equation term, which is progressively removed during training to distil an interpretable Lindbladian solution. Our method reliably learns open-system dynamics across neutral-atom (with 2D connectivity) and superconducting Hamiltonians, as well as the Heisenberg XYZ, and PXP models on a spin-1/2 chain. For the dissipative part, we show robustness over phase noise, thermal noise, and their combination. Our algorithm can robustly infer these dissipative systems over noise-to-signal ratios spanning four orders of magnitude, and system sizes up to $N=6$ qubits with fewer than $5 \times 10^5$ shots.
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Submitted 8 March, 2026;
originally announced March 2026.
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Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equations
Authors:
Timothy Heightman,
Edward Jiang,
Antonio Acín
Abstract:
Understanding and characterising quantum many-body dynamics remains a significant challenge due to both the exponential complexity required to represent quantum many-body Hamiltonians, and the need to accurately track states in time under the action of such Hamiltonians. This inherent complexity limits our ability to characterise quantum many-body systems, highlighting the need for innovative appr…
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Understanding and characterising quantum many-body dynamics remains a significant challenge due to both the exponential complexity required to represent quantum many-body Hamiltonians, and the need to accurately track states in time under the action of such Hamiltonians. This inherent complexity limits our ability to characterise quantum many-body systems, highlighting the need for innovative approaches to unlock their full potential. To address this challenge, we propose a novel method to solve the Hamiltonian Learning (HL) problem-inferring quantum dynamics from many-body state trajectories-using Neural Differential Equations combined with an Ansatz Hamiltonian. Our method is reliably convergent, experimentally friendly, and interpretable, making it a stable solution for HL on a set of Hamiltonians previously unlearnable in the literature. In addition to this, we propose a new quantitative benchmark based on power laws, which can objectively compare the reliability and generalisation capabilities of any two HL algorithms. Finally, we benchmark our method against state-of-the-art HL algorithms with a 1D spin-1/2 chain proof of concept.
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Submitted 16 August, 2024;
originally announced August 2024.
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FeMnNiAlCr High Entropy Alloys with High-Efficiency Surface Oxide Solar Absorbers for Concentrating Solar Power Systems
Authors:
Xiaoxue Gao,
Edwin Jiang,
Andrew Pike,
Eldred Lee,
Margaret Wu,
Huan Wang,
Sheppard Somers,
Weiyang Li,
Geoffroy Hautier,
Ian Baker,
Jifeng Liu
Abstract:
High entropy alloys (HEAs) have attracted substantial interest in recent years. Thus far, most investigations have focused on their applications as structural materials rather than functional materials. In this paper, we show that FeMnNiAlCr HEAs can potentially be applied as both a structural and functional material for high-efficiency concentrated solar thermal power (CSP) systems working at >70…
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High entropy alloys (HEAs) have attracted substantial interest in recent years. Thus far, most investigations have focused on their applications as structural materials rather than functional materials. In this paper, we show that FeMnNiAlCr HEAs can potentially be applied as both a structural and functional material for high-efficiency concentrated solar thermal power (CSP) systems working at >700 degrees C. The HEA itself would be used in high-temperature tubing to carry working fluids, while its surface oxide would act as a high-efficiency solar thermal absorber. These HEAs have demonstrated yield strengths 2-3x greater than that of stainless steel at 700 degrees C and a creep lifetime >800 h at 700 degrees C under a typical CSP tubing mechanical load of 35 MPa. Their Mn-rich surface oxides maintain a high optical-to-thermal conversion efficiency of ~87% under 1000x solar concentration for 20 simulated day-night thermal cycles between 750 degrees C and environmental temperature. These HEAs have also sustained immersion in unpurified bromide molten salts for 14 days at 750°C with <2% weight loss, in contrast to 70% weight loss from a 316 stainless steel reference. The simultaneous achievement of promising mechanical, optical, and thermochemical properties in this FeMnNiAlCr system opens the door to new applications of HEAs in solar energy harvesting.
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Submitted 22 December, 2023;
originally announced December 2023.
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The Volume Rule in the Random Packing Ratio
Authors:
Honghai Liu,
Enyong Jiang,
Chang Q. Sun,
Bruce Z. Gao
Abstract:
The study on the relationship between the spheres and voids in packing system suggests that the edge effect at the interface between the container and the particles is an important factor lowering the packing ratio. To pack spheres in a container with high packing ratio, an optimized sphere size and an optimized sequence of sphere sizes exist for the packing of single-sized and multi-sized spheres…
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The study on the relationship between the spheres and voids in packing system suggests that the edge effect at the interface between the container and the particles is an important factor lowering the packing ratio. To pack spheres in a container with high packing ratio, an optimized sphere size and an optimized sequence of sphere sizes exist for the packing of single-sized and multi-sized spheres, respectively. We suggest that the concepts of volume and contact should be clearly defined for the packing problem in specific scale.
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Submitted 21 May, 2012;
originally announced May 2012.
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Nature evolution of the shortened bond in atomic clusters and at junction interfaces
Authors:
Chang Q. Sun,
C. M. Li,
Y. Shi,
Z. Q. Li,
H. L. Bai,
E. Y. Jiang
Abstract:
Thermally stimulated process such as evaporation, phase transition, or solid-liquid transition of a solid consumes each a certain portion of the solid cohesive energy that is the sum of bond energy over all the coordinates of all the involved atoms. Generally, the critical temperatures for such processes drop with solid size, unless hetero capping or interfacial interaction becomes dominant, bec…
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Thermally stimulated process such as evaporation, phase transition, or solid-liquid transition of a solid consumes each a certain portion of the solid cohesive energy that is the sum of bond energy over all the coordinates of all the involved atoms. Generally, the critical temperatures for such processes drop with solid size, unless hetero capping or interfacial interaction becomes dominant, because of the increased portion of the lower-coordinated surface atoms [Sun et al., J. Phys. Chem. B108, 1080 (2004)]. It is intriguing, however, that the melting point (Tm) of a solid containing III-A or IV-A atoms oscillates with size (the Tm drops first and then rises as the solid size is reduced) and that the Tm of chemically capped nanosolid often increases with the inverse size. Here we show that bond nature evolution is essential for the selective nanosolids and at the junction interfaces, which is responsible for the superheating of the smallest nanosolids, chemically capped clusters, and junction interfaces that exhibit insulating nature with high mechanical strength.
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Submitted 12 June, 2005; v1 submitted 4 June, 2005;
originally announced June 2005.