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| Autores principales: | , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.07373 |
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| _version_ | 1866911563311480832 |
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| author | Lesser, Omri Liu, Yanjun Maus, Natalie Panigrahi, Aaditya Mallayya, Krishnanand Gong, Albert Kabra, Anmol Lee, Scott B. Chatterjee, Sudipta Merino, Amira Weinberger, Kilian Q. Schoop, Leslie M. Gardner, Jacob R. Kim, Eun-Ah |
| author_facet | Lesser, Omri Liu, Yanjun Maus, Natalie Panigrahi, Aaditya Mallayya, Krishnanand Gong, Albert Kabra, Anmol Lee, Scott B. Chatterjee, Sudipta Merino, Amira Weinberger, Kilian Q. Schoop, Leslie M. Gardner, Jacob R. Kim, Eun-Ah |
| contents | 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 a structure- and chemistry-aware approach implemented in an interpretable Gaussian process model, which we call GP-$T_c$ (Gaussian Process $T_c$), that enables uncertainty-quantified prediction of superconductivity from experimentally accessible inputs. By encoding local bonding environments and geometry as graphlet histograms and learning within a probabilistic framework, we find that the predictive space collapses to a compact set of descriptors: the distribution of electron-affinity differences between neighboring atoms, together with simple elemental features and interatomic distances, provides an informative basis for predicting $T_c$ across disparate superconducting families. This result identifies an overlooked chemical control parameter while emphasizing the essential role of local structure beyond composition-only approaches. We demonstrate the framework through two complementary tests: validation against a recently established superconducting family and discovery of a previously unknown material. GP-$T_c$ reproduces the experimentally reported $T_c$ range of the infinite-layer nickelate Nd0.8Sr0.2NiO2. We further predict superconductivity in stoichiometric PtPb$_3$Bi and experimentally confirm it through synthesis and bulk measurements, establishing PtPb$_3$Bi as a new superconductor with $T_c$~3 K. GP-$T_c$ identifies additional high-priority superconducting candidates -- including SrNiO2, K(PRh)2, and Ho2C3 -- that provide concrete targets for ongoing and future experimental exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_07373 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Electron affinity difference distributions guide the discovery of the superconductor PtPb$_3$Bi Lesser, Omri Liu, Yanjun Maus, Natalie Panigrahi, Aaditya Mallayya, Krishnanand Gong, Albert Kabra, Anmol Lee, Scott B. Chatterjee, Sudipta Merino, Amira Weinberger, Kilian Q. Schoop, Leslie M. Gardner, Jacob R. Kim, Eun-Ah Superconductivity Materials Science 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 a structure- and chemistry-aware approach implemented in an interpretable Gaussian process model, which we call GP-$T_c$ (Gaussian Process $T_c$), that enables uncertainty-quantified prediction of superconductivity from experimentally accessible inputs. By encoding local bonding environments and geometry as graphlet histograms and learning within a probabilistic framework, we find that the predictive space collapses to a compact set of descriptors: the distribution of electron-affinity differences between neighboring atoms, together with simple elemental features and interatomic distances, provides an informative basis for predicting $T_c$ across disparate superconducting families. This result identifies an overlooked chemical control parameter while emphasizing the essential role of local structure beyond composition-only approaches. We demonstrate the framework through two complementary tests: validation against a recently established superconducting family and discovery of a previously unknown material. GP-$T_c$ reproduces the experimentally reported $T_c$ range of the infinite-layer nickelate Nd0.8Sr0.2NiO2. We further predict superconductivity in stoichiometric PtPb$_3$Bi and experimentally confirm it through synthesis and bulk measurements, establishing PtPb$_3$Bi as a new superconductor with $T_c$~3 K. GP-$T_c$ identifies additional high-priority superconducting candidates -- including SrNiO2, K(PRh)2, and Ho2C3 -- that provide concrete targets for ongoing and future experimental exploration. |
| title | Electron affinity difference distributions guide the discovery of the superconductor PtPb$_3$Bi |
| topic | Superconductivity Materials Science |
| url | https://arxiv.org/abs/2510.07373 |