Lubin Wang | Search arXiv

arXiv2026

Hidden magnetic order within the pressure induced superconducting dome of UTe2

Unconventional superconductivity typically occurs near magnetic instabilities, and the corresponding spin fluctuations are widely believed to play a crucial role in mediating electron pairing. UTe$_2$ is a promising candidate for exhibiting multiple spin-triplet superconducting phases when tuning with applied pressure and magnetic fields, but the nature of the magnetism driving these unconventional pairing states is undetermined. Our measurements of UTe$_2$ under applied pressures and magnetic fields reveal the presence of a magnetic order hidden within the pressure-induced superconducting dome, which vanishes together with the superconductivity once there is sufficiently high pressure to induce the three-dimensional antiferromagnetic phase. Extrapolation of the phase boundary of the hidden magnetic order, which is most likely antiferromagnetic in nature, points to a zero-temperature quantum critical point that coincides with the maximum transition temperature of the pressure-induced superconducting dome, suggesting that it could corresponds to the parent magnetic phase of the critical antiferromagnetic spin fluctuations driving the triplet superconductivity. These findings advance the understanding of the interplay of magnetism and superconductivity in an exemplar candidate triplet superconductor, which is necessary for revealing the microscopic origin of the different unconventional superconducting phases.

cond-mat.supr-con↗

arXiv2022

Data-driven Topology Optimization (DDTO) for Three-dimensional Continuum Structures

Developing appropriate analytic-function-based constitutive models for new materials with nonlinear mechanical behavior is demanding. For such kinds of materials, it is more challenging to realize the integrated design from the collection of the material experiment under the classical topology optimization framework based on constitutive models. The present work proposes a mechanistic-based data-driven topology optimization (DDTO) framework for three-dimensional continuum structures under finite deformation. In the DDTO framework, with the help of neural networks and explicit topology optimization method, the optimal design of the three-dimensional continuum structures under finite deformation is implemented only using the uniaxial and equi-biaxial experimental data. Numerical examples illustrate the effectiveness of the data-driven topology optimization approach, which paves the way for the optimal design of continuum structures composed of novel materials without available constitutive relations.

math.OC↗