We perform a high-throughput computational search for novel phonon-mediated superconductors, starting from the Materials Cloud three-dimensional structure database of experimentally known inorganic stoichiometric compounds. We first compute the Allen-Dynes critical temperature ( T c AD ) for 4533 nonmagnetic metals using a direct and progressively finer sampling of the electron-phonon couplings. For the candidates with the largest T c AD value, we use automated Wannierizations and electron-phonon interpolations to obtain a high-quality data set for the most promising 250 dynamically stable structures, for which we calculate spectral functions, superconducting band gaps, and isotropic Migdal-Eliashberg critical temperatures. For 140 of these, we also provide anisotropic Migdal-Eliashberg superconducting gaps and critical temperatures. The approach is remarkably successful in finding known superconductors and we find 24 unknown ones with a predicted anisotropic T c value above 10 K. Among them, we identify a possible double-gap superconductor ( p -doped Ba B 2 ), a nonmagnetic half-Heusler Zr Ru Sb , and the perovskite Ta Ru 3 C , all exhibiting significant T c values. Finally, we introduce a sensitivity analysis to estimate the robustness of the predictions.

While machine learning operations (MLOps) have received significant interest, much less work has been published addressing MLOps in industrial production settings lately, particularly if solutions are not cloud based. This article addresses this shortcoming based on our and our partner’s real industrial experience across different application domains.

Hysteretic nonlinear elasticity is often observed in consolidated granular media, including concrete, mortar, sandstones, or rocks. Nonlinearity is frequently quantified using Nonlinear Resonant Ultrasonic Spectroscopy (NRUS), which provides tools to define nonlinear parameters for both fast and slow dynamic effects, often observed when analyzing the propagation velocity dependence on strain in such materials. The dependence of these parameters on temperature was studied with the aim of using NRUS to quantify the induced thermal damage; thus, experiments were performed spanning a wide temperature range. However, since most of these materials are used in construction (concrete and sandstone, mostly), it is of interest to understand how sensitive the measured nonlinear parameters are to small environmental temperature fluctuations. In this paper, the dependence on temperature of elastic parameters is investigated, both linear (wave velocity and damping) and nonlinear (the slope and hysteresis of the curves describing the strain dependence of wave velocity and residual conditioning effect on wave velocity), separating the slow from the fast dynamic properties of nonlinearity. The observations reported here denote a different behavior for concrete and Berea sandstone.

Physics-informed neural networks (PINNs) on complex domains are limited by input representations that encode geometry but do not reflect the physics of the governing PDE. We propose an operator-aware PINN for solid mechanics problems that embeds precomputed eigenmodes of the problem’s own discrete operator as geometry and physics-aware features within a weak-form variational formulation. The displacement field is represented by a neural mapping enriched by operator-aligned features rather than being constrained to a finite element trial space. A hybrid chain-rule formulation propagates spatial derivatives through both coordinate and eigenmode branches, yielding strain fields consistent with operator structure while preserving neural expressivity. The approach maintains the stability of variational formulations while mitigating the spectral bias and geometry-encoding limitations of classical PINNs. For inverse elasticity, the same eigenmodes act as virtual test functions in a weak-form residual, mitigating the stiffness-collapse pathology. The framework provides an instance-based alternative to differentiable finite element method without labeled training data. It achieves competitive accuracy and connects spectral operator theory with physics-informed neural computation.