Amira Merino | Searcharxiv

arXiv2026

New Superconductors in the PtPb$_3$Bi Structure Type

The quest for new superconductors is of both fundamental and technological importance. Recently, an artificial intelligence method correctly predicted PtPb$_3$Bi to be a superconductor. In this work, we find superconductivity in the newly synthesized $M$Pb$_{4-x}$Bi$_x$ ($M$ = Au, Pd, and Rh), of which PtPb$_3$Bi is a member. When $M$ = Ni, whose radius is considerably smaller, the structure instead collapses into the different, Pb-substituted NiBi$_3$ type. Interestingly, the stoichiometric parameter $x$ shifts across the three compounds to keep the total valence electron count close to 20 per formula unit. The superconducting transitions occur at 4.9, 4.2, and 3.4 K, for $M$ = Au, Pd, and Rh, respectively. Using electrical resistivity, magnetization, and specific heat measurements, we establish the bulk nature of the superconducting state and determine the critical fields, characteristic length scales, and anisotropy ratios. All three compounds are moderately anisotropic type-II superconductors, with modest upper critical field anisotropies of $H_{c2}^{\parallel c}/H_{c2}^{\perp c} \approx 1.2$ to $1.5$. These results establish $M$Pb$_{4-x}$Bi$_x$ as a family of anisotropic superconductors and a platform for studying how site disorder and Pb-Bi mixing govern superconductivity in heavy-element intermetallics.

cond-mat.supr-con↗

arXiv2025

Electron-affinity difference distributions as an organizing principle for superconductivity, enabling the discovery of PtPb$_3$Bi

Predicting the superconducting transition temperature ($T_c$) from crystal structure and composition remains a central challenge in condensed-matter physics, reflecting the absence of a broadly predictive framework connecting microscopic bonding to macroscopic quantum behavior. Here, we introduce $\mathcal{GP}$-$T_c$, an interpretable, structure- and chemistry-aware Gaussian process model that enables uncertainty-quantified $T_c$ prediction from experimentally accessible inputs. By encoding local bonding environments as graphlet histograms, we find that the predictive space collapses to a compact set of descriptors: the distribution of electron-affinity (EA) differences between neighboring atoms, together with interatomic distances and simple elemental features, suffices to predict $T_c$ across disparate superconducting families---identifying an overlooked chemical control parameter that underscores the essential role of local structure beyond composition-only approaches. Our results demonstrate that the EA differences serves as an accessible window into electronic structure providing a mechanism-agnostic physical basis that captures $T_c$ across conventional and unconventional families, including doped charge transfer insulators. $\mathcal{GP}$-$T_c$ reproduces the experimentally reported $T_c$ range of the infinite-layer nickelate Nd$_{0.8}$Sr$_{0.2}$NiO$_2$, and we predict and experimentally confirm superconductivity in stoichiometric PtPb$_3$Bi ($T_c \approx 3$~K). To facilitate broad community use, $\mathcal{GP}$-$T_c$ is made available through a web interface for crystal-structure-based prediction, and the same framework identifies additional high-priority superconducting candidates---including SrNiO$_2$ and K(PRh)$_2$---that provide concrete targets for ongoing and future experimental exploration.

cond-mat.supr-con↗