arXiv subjects

Shuchen Li

Publications and source records attributed to Shuchen Li.

7 recordsLinked to original sources

arXiv2026

Electronic-Structure Correlations Governing Superconductivity in Nb-Based High-Entropy Alloys

Superconducting high-entropy alloys have recently emerged as a new platform for exploring superconductivity in highly disordered metallic systems and may offer advantages for applications requiring mechanical robustness and tolerance to extreme environments. Yet the mechanisms that govern their superconductivity, particularly the roles of lattice distortion and complex local order, both inherent to high-entropy alloys, remain unclear. The conventional valence-electron-concentration rule fails to reliably predict superconducting behavior, motivating a correlation analysis that links performance to electronic structure and lattice disorder. Here, we study a systematic series of niobium-based body-centered-cubic high-entropy alloys, from binary to quinary compositions, designed to investigate the electronic and structural effects and identify the dominant factors controlling superconductivity. Our experimental results reveal that the superconducting critical properties evolve non-monotonically with alloy complexity. Interestingly, alloys with greater lattice distortion can still achieve higher critical temperature and upper critical field. These observations are corroborated by first-principles and Eliashberg analyses, which identify the position of the niobium d-band relative to the Fermi level as the primary driver of electron-phonon coupling, critical temperature, and upper critical field, with lattice distortion serving as a secondary modifier that generally weakens coupling. We consolidate these findings into a detailed correlation map linking superconducting properties to electronic-structure fingerprints and vibrational signatures, establishing a mechanism-informed design strategy for superconducting high-entropy alloys with enhanced critical temperature and field.

cond-mat.supr-con↗

arXiv2026

Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms

In this work, we give a ${\rm poly}(d,k)$ time and sample algorithm for efficiently learning the parameters of a mixture of $k$ spherical distributions in $d$ dimensions. Unlike all previous methods, our techniques apply to heavy-tailed distributions and include examples that do not even have finite covariances. Our method succeeds whenever the cluster distributions have a characteristic function with sufficiently heavy tails. Such distributions include the Laplace distribution but crucially exclude Gaussians. All previous methods for learning mixture models relied implicitly or explicitly on the low-degree moments. Even for the case of Laplace distributions, we prove that any such algorithm must use super-polynomially many samples. Our method thus adds to the short list of techniques that bypass the limitations of the method of moments. Somewhat surprisingly, our algorithm does not require any minimum separation between the cluster means. This is in stark contrast to spherical Gaussian mixtures where a minimum $\ell_2$-separation is provably necessary even information-theoretically [Regev and Vijayaraghavan '17]. Our methods compose well with existing techniques and allow obtaining ''best of both worlds" guarantees for mixtures where every component either has a heavy-tailed characteristic function or has a sub-Gaussian tail with a light-tailed characteristic function. Our algorithm is based on a new approach to learning mixture models via efficient high-dimensional sparse Fourier transforms. We believe that this method will find more applications to statistical estimation. As an example, we give an algorithm for consistent robust mean estimation against noise-oblivious adversaries, a model practically motivated by the literature on multiple hypothesis testing. It was formally proposed in a recent Master's thesis by one of the authors, and has already inspired follow-up works.

cs.DS↗

arXiv2025

Non-reciprocal Magnetoresistances in Chiral Tellurium

Materials with broken fundamental symmetries, such as chiral crystals, provide a rich playground for exploring unconventional spin-dependent transport phenomena. The interplay between a material's chirality, strong spin-orbit coupling, and charge currents can lead to complex non-reciprocal effects, where electrical resistance depends on the direction of current and magnetic fields. In this study, we systematically investigate the angular dependencies of magnetoresistance in single-crystalline chiral Tellurium (Te). We observe distinct non-reciprocal magnetoresistances for magnetic fields applied along three orthogonal directions: parallel to the current along the chiral axis (z), in the sample plane but perpendicular to the current (y), and out of the sample plane (x). Through detailed analysis of the chirality- and thickness-dependence of the signals, we successfully disentangle multiple coexisting mechanisms. We conclude that the Edelstein effect, arising from the chiral structure's radial spin texture, is responsible for the non-reciprocity along the z-axis. In contrast, the chirality-independent signal along the y-axis is attributed to the Nernst effect, and the non-reciprocity along the x-axis may originate from intrinsic orbital magnetizations. These findings elucidate the complex interplay of spin, orbital, and thermal effects in Te, providing a complete picture of its non-reciprocal transport properties.

cond-mat.mtrl-sci↗

arXiv2024

On the Hardness of Learning One Hidden Layer Neural Networks

In this work, we consider the problem of learning one hidden layer ReLU neural networks with inputs from $\mathbb{R}^d$. We show that this learning problem is hard under standard cryptographic assumptions even when: (1) the size of the neural network is polynomial in $d$, (2) its input distribution is a standard Gaussian, and (3) the noise is Gaussian and polynomially small in $d$. Our hardness result is based on the hardness of the Continuous Learning with Errors (CLWE) problem, and in particular, is based on the largely believed worst-case hardness of approximately solving the shortest vector problem up to a multiplicative polynomial factor.

cs.LG↗

arXiv2024

Unconventional Spin-Orbit Torques from Sputtered MoTe2 Films

Materials with strong spin-orbit coupling and low crystalline symmetry are promising for generating large unconventional spin-orbit torques (SOTs), such as in-plane field-like (FL) torques and out-of-plane damping-like (DL) torques, which can effectively manipulate and deterministically switch an out-of-plane magnetization without the need for additional external in-plane magnetic fields. Here, we report SOTs generated by magnetron-sputtered 1T' MoTe2/Permalloy (Py; Ni80Fe20)/MgO heterostructures using both spin-torque ferromagnetic resonance (ST-FMR) and second harmonic Hall measurements. We observed unconventional FL and DL torques in our samples due to spins polarized normal to the interface of MoTe2 and Py layers, and studied the influence of crystallographic order and MoTe2 layer thickness on the SOTs. By comparing the Raman spectra of 1T' MoTe2 samples prepared in different ways, we found a tensile strain in sputtered MoTe2 films, which might further enhance the generation of unconventional torques by reducing the symmetry of 1T' MoTe2.

cond-mat.mtrl-sci↗

arXiv2024

Strong Damping-Like Torques in Wafer-Scale MoTe${}_2$ Grown by MOCVD

The scalable synthesis of strong spin orbit coupling (SOC) materials such as 1T${}^\prime$ phase MoTe${}_2$ is crucial for spintronics development. Here, we demonstrate wafer-scale growth of 1T${}^\prime$ MoTe${}_2$ using metal-organic chemical vapor deposition (MOCVD) with sputtered Mo and (C${}_4$H${}_9$)${}_2$Te. The synthesized films show uniform coverage across the entire sample surface. By adjusting the growth parameters, a synthesis process capable of producing 1T${}^\prime$ and 2H MoTe${}_2$ mixed phase films was achieved. Notably, the developed process is compatible with back-end-of-line (BEOL) applications. The strong spin-orbit coupling of the grown 1T${}^\prime$ MoTe${}_2$ films was demonstrated through spin torque ferromagnetic resonance (ST-FMR) measurements conducted on a 1T${}^\prime$ MoTe${}_2$/permalloy bilayer RF waveguide. These measurements revealed a significant damping-like torque in the wafer-scale 1T${}^\prime$ MoTe${}_2$ film and indicated high spin-charge conversion efficiency. The BEOL compatible process and potent spin orbit torque demonstrate promise in advanced device applications.

cond-mat.mtrl-sci↗

arXiv2024

Epitaxial growth and magnetic properties of kagome metal FeSn/elemental ferromagnet heterostructures

Binary kagome compounds TmXn (T = Mn, Fe, Co; X = Sn, Ge; m:n = 3:1, 3:2, 1:1) have garnered recent interest owing to the presence of both topological band crossings and flat bands arising from the geometry of the metal-site kagome lattice. To exploit these electronic features for potential applications in spintronics, the growth of high quality heterostructures is required. Here we report the synthesis of Fe/FeSn and Co/FeSn bilayers on Al2O3 substrates using molecular beam epitaxy to realize heterointerfaces between elemental ferromagnetic metals and antiferromagnetic kagome metals. Structural characterization using high-resolution X-ray diffraction, reflection high-energy electron diffraction, and electron microscopy reveals the FeSn films are flat and epitaxial. Rutherford backscattering spectroscopy was used to confirm the stoichiometric window where the FeSn phase is stabilized, while transport and magnetometry measurements were conducted to verify metallicity and magnetic ordering in the films. Exchange bias was observed, confirming the presence of antiferromagnetic order in the FeSn layers, paving the way for future studies of magnetism in kagome heterostructures and potential integration of these materials into devices.

cond-mat.mtrl-sci↗