{"topic":"superconductor-discovery","sort":"top","limit":25,"offset":0,"count":25,"papers":[{"id":"arxiv:1709.02727","kind":"article","title":"Machine learning modeling of superconducting critical temperature","authors":["Valentin Stanev","Corey Oses","A. Gilad Kusne","Efrain Rodriguez","Johnpierre Paglione","Stefano Curtarolo A","Ichiro Takeuchi"],"author_count":7,"year":2018,"published":"2018-06-22","venue":"npj Computational Materials","cited_by":499,"doi":"10.1038/s41524-018-0085-8","arxiv_id":"1709.02727","nct_id":null,"source_url":"https://doi.org/10.1038/s41524-018-0085-8","archive_url":"https://space-kitty.com/papers/arxiv:1709.02727","updated":"2017-10-06","pmid":null,"pmcid":null,"openalex_id":"W2751488329","pdf_url":"https://www.nature.com/articles/s41524-018-0085-8.pdf","abstract":"Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ($T_{\\mathrm{c}}$) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their $T_{\\mathrm{c}}$ values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of $T_{\\mathrm{c}}$ for cuprate, iron-based, and \"low-$T_{\\mathrm{c}}$\" compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify more than 30 non-cuprate and non-iron-based oxides as candidate materials.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1016/j.commatsci.2019.109203","kind":"article","title":"Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation","authors":["Zheng Xiong","Yuxin Cui","Zhonghao Liu","Yong Zhao","Ming Hu","Jianjun Hu"],"author_count":6,"year":2019,"published":"2019-08-27","venue":"Computational Materials Science","cited_by":337,"doi":"10.1016/j.commatsci.2019.109203","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.commatsci.2019.109203","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.commatsci.2019.109203","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2970591347","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1604.03525","kind":"article","title":"EPW: Electron-phonon coupling, transport and superconducting properties using maximally localized Wannier functions","authors":["Samuel Poncé","Elena R. Margine","Carla Verdi","Feliciano Giustino"],"author_count":4,"year":2016,"published":"2016-04-12","venue":"Computer Physics Communications","cited_by":1289,"doi":"10.1016/j.cpc.2016.07.028","arxiv_id":"1604.03525","nct_id":null,"source_url":"https://arxiv.org/abs/1604.03525","archive_url":"https://space-kitty.com/papers/arxiv:1604.03525","updated":"2016-07-17","pmid":null,"pmcid":null,"openalex_id":"W2339223279","pdf_url":"https://arxiv.org/pdf/1604.03525","abstract":"The EPW (Electron-Phonon coupling using Wannier functions) software is a Fortran90 code that uses density-functional perturbation theory and maximally localized Wannier functions for computing electron-phonon couplings and related properties in solids accurately and efficiently. The EPW v4 program can be used to compute electron and phonon self-energies, linewidths, electron-phonon scattering rates, electron-phonon coupling strengths, transport spectral functions, electronic velocities, resistivity, anisotropic superconducting gaps and spectral functions within the Migdal-Eliashberg theory. The code now supports spin-orbit coupling, time-reversal symmetry in non-centrosymmetric crystals, polar materials, and $\\mathbf{k}$ and $\\mathbf{q}$-point parallelization. Considerable effort was dedicated to optimization and parallelization, achieving almost a ten times speedup with respect to previous releases. A computer test farm was implemented to ensure stability and portability of the code on the most popular compilers and architectures. Since April 2016, version 4 of the EPW code is fully integrated in and distributed with the Quantum ESPRESSO package, and can be downloaded through QE-forge at http://qe-forge.org/gf/project/q-e.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.mtrl-sci"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:0803.2703","kind":"article","title":"Is LaO$_{1-x}$F$_x$FeAs an electron-phonon superconductor ?","authors":["L. Boeri","O. V. Dolgov","A. A. Golubov"],"author_count":3,"year":2008,"published":"2008-03-18","venue":"Physical Review Letters, 101, 026403 (2008)","cited_by":605,"doi":"10.1103/physrevlett.101.026403","arxiv_id":"0803.2703","nct_id":null,"source_url":"https://arxiv.org/abs/0803.2703","archive_url":"https://space-kitty.com/papers/arxiv:0803.2703","updated":"2008-03-18","pmid":null,"pmcid":null,"openalex_id":"W1581444642","pdf_url":"https://arxiv.org/pdf/0803.2703","abstract":"In this paper we calculate the electron-phonon coupling of the newly-discovered superconductor LaO$_{1-x}$F$_x$FeAs from first-principles, using Density Functional Perturbation Theory. For pure LaOFeAs, the calculated electron-phonon coupling constant $λ=0.21$ and logarithmic-averaged frequency $ω_{ln}=206 K$, give a maximum $T_c$ of 0.8 K, using the standard Migdal-Eliashberg theory. For the $F-$doped compounds, we predict even smaller coupling constants, due to the strong suppression of the electronic Density of States at the Fermi level. To reproduce the experimental $T_c=26 K$, a 5-6 times larger coupling constant would be needed. Our results indicate that electron-phonon coupling is not sufficient to explain superconductivity in the newly-discovered LaO$_{1-x}$F$_x$FeAs superconductor, probably due to the importance of strong correlation effects.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con","cond-mat.str-el"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:cond-mat/0111182","kind":"article","title":"First-Principles Calculation of the Superconducting Transition in MgB2 within the Anisotropic Eliashberg Formalism","authors":["Hyoung Joon Choi","David Roundy","Hong Sun","Marvin L. Cohen","Steven G. Louie"],"author_count":5,"year":2001,"published":"2001-11-09","venue":"Phys.Rev.B66:020513,2002","cited_by":383,"doi":"10.1103/physrevb.66.020513","arxiv_id":"cond-mat/0111182","nct_id":null,"source_url":"https://arxiv.org/abs/cond-mat/0111182","archive_url":"https://space-kitty.com/papers/arxiv:cond-mat/0111182","updated":"2001-11-09","pmid":null,"pmcid":null,"openalex_id":"W2037180571","pdf_url":"https://arxiv.org/pdf/cond-mat/0111182","abstract":"We present a study of the superconducting transition in MgB2 using the ab-initio pseudopotential density functional method and the fully anisotropic Eliashberg equation. Our study shows that the anisotropic Eliashberg equation, constructed with ab-initio calculated momentum-dependent electron-phonon interaction and anharmonic phonon frequencies, yields an average electron-phonon coupling constant lambda = 0.61, a transition temperature Tc = 39 K, and a boron isotope-effect exponent alphaB = 0.31 with a reasonable assumption of mu* = 0.12. The calculated values for Tc, lambda, and alphaB are in excellent agreement with transport, specific heat, and isotope effect measurements respectively. The individual values of the electron-phonon coupling lambda(k,k') on the various pieces of the Fermi surface however vary from 0.1 to 2.5. The observed Tc is a result of both the raising effect of anisotropy in the electron-phonon couplings and the lowering effect of anharmonicity in the relevant phonon modes.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1007/s10948-020-05682-0","kind":"article","title":"Machine Learning F-Doped Bi(Pb)–Sr–Ca–Cu–O Superconducting Transition Temperature","authors":["Yun Zhang","Xiaojie Xu"],"author_count":2,"year":2020,"published":"2020-09-21","venue":"Journal of Superconductivity and Novel Magnetism","cited_by":109,"doi":"10.1007/s10948-020-05682-0","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s10948-020-05682-0","archive_url":"https://space-kitty.com/papers/doi:10.1007/s10948-020-05682-0","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3087103549","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Physics of Superconductivity and Magnetism"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1515/ijmr-2020-7986","kind":"article","title":"Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning","authors":["Yun Zhang","Xiaojie Xu"],"author_count":2,"year":2021,"published":"2021-01-01","venue":"International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde)","cited_by":98,"doi":"10.1515/ijmr-2020-7986","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1515/ijmr-2020-7986","archive_url":"https://space-kitty.com/papers/doi:10.1515/ijmr-2020-7986","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3130656729","pdf_url":"https://www.degruyter.com/document/doi/10.1515/ijmr-2020-7986/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1038/s41524-021-00666-7","kind":"article","title":"Machine learning of superconducting critical temperature from Eliashberg theory","authors":["Stephen Xie","Yundi Quan","Ajinkya C. Hire","B. Deng","Jonathan M. DeStefano","I. Salinas","U. S. Shah","Laura Fanfarillo","Jinhyuk Lim","J. Kim","Gregory R. Stewart","J. J. Hamlin","P. J. Hirschfeld","Richard G. Hennig"],"author_count":14,"year":2022,"published":"2022-01-25","venue":"npj Computational Materials","cited_by":86,"doi":"10.1038/s41524-021-00666-7","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1038/s41524-021-00666-7","archive_url":"https://space-kitty.com/papers/doi:10.1038/s41524-021-00666-7","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3167076346","pdf_url":"https://www.nature.com/articles/s41524-021-00666-7.pdf","abstract":"Abstract The Eliashberg theory of superconductivity accounts for the fundamental physics of conventional superconductors, including the retardation of the interaction and the Coulomb pseudopotential, to predict the critical temperature T c . McMillan, Allen, and Dynes derived approximate closed-form expressions for the critical temperature within this theory, which depends on the electron–phonon spectral function α 2 F ( ω ). Here we show that modern machine-learning techniques can substantially improve these formulae, accounting for more general shapes of the α 2 F function. Using symbolic regression and the SISSO framework, together with a database of artificially generated α 2 F functions and numerical solutions of the Eliashberg equations, we derive a formula for T c that performs as well as Allen–Dynes for low- T c superconductors and substantially better for higher- T c ones. This corrects the systematic underestimation of T c while reproducing the physical constraints originally outlined by Allen and Dynes. This equation should replace the Allen–Dynes formula for the prediction of higher-temperature superconductors.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Machine learning of superconducting critical temperature from Eliashberg theory\" by Stephen Xie, Yundi Quan, Ajinkya C. Hire et al., https://doi.org/10.1038/s41524-021-00666-7, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:2001.09852","kind":"article","title":"Predicting novel superconducting hydrides using machine learning approaches","authors":["Michael Hutcheon","Alice M. Shipley","Richard J. Needs"],"author_count":3,"year":2020,"published":"2020-04-22","venue":"Physical review. B./Physical review. B","cited_by":77,"doi":"10.1103/physrevb.101.144505","arxiv_id":"2001.09852","nct_id":null,"source_url":"https://doi.org/10.1103/physrevb.101.144505","archive_url":"https://space-kitty.com/papers/arxiv:2001.09852","updated":"2020-04-08","pmid":null,"pmcid":null,"openalex_id":"W3002557233","pdf_url":"https://arxiv.org/pdf/2001.09852","abstract":"Searching for superconducting hydrides has so far largely focused on finding materials exhibiting the highest possible critical temperatures ($T_c$). This has led to a bias towards materials stabilised at very high pressures, which introduces a number of technical difficulties in experiment. Here we apply machine learning methods in an effort to identify superconducting hydrides which can operate closer to ambient conditions. The output of these models informs structure searches, from which we identify and screen stable candidates before performing electron-phonon calculations to obtain $T_c$. Hydrides of alkali and alkaline earth metals are identified as particularly promising; a $T_c$ of up to 115 K is calculated for RbH$_{12}$ at 50 GPa and a $T_c$ of up to 90 K is calculated for CsH$_7$ at 100 GPa.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2002.07266","kind":"article","title":"Predicting new superconductors and their critical temperatures using machine learning","authors":["Benjamin Roter","Sasa V. Dordevic"],"author_count":2,"year":2020,"published":"2020-05-27","venue":"Physica C Superconductivity","cited_by":73,"doi":"10.1016/j.physc.2020.1353689","arxiv_id":"2002.07266","nct_id":null,"source_url":"https://doi.org/10.1016/j.physc.2020.1353689","archive_url":"https://space-kitty.com/papers/arxiv:2002.07266","updated":"2020-02-17","pmid":null,"pmcid":null,"openalex_id":"W3005735954","pdf_url":"https://arxiv.org/pdf/2002.07266","abstract":"We used the superconductors in the SuperCon database to construct element vectors and then perform unsupervised learning of their critical temperatures (T$_c$). Only the chemical composition of superconductors was used in this procedure. No physical predictors (neither experimental nor computational) of any kind were used. We achieved the coefficient of determination R$^2$$\\simeq$0.93, which is comparable and in some cases higher then similar estimates using other artificial intelligence techniques. Based on this machine learning model, we predicted several new superconductors with high critical temperatures. We also discuss the factors that limit the learning process and suggest possible ways to overcome them.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:cond-mat/0211084","kind":"article","title":"Phonon dispersion and electron-phonon interaction for YBa_2Cu_3O_7 from first-principles calculations","authors":["K. -P. Bohnen","R. Heid","M. Krauss"],"author_count":3,"year":2002,"published":"2002-11-05","venue":"Europhysics Letters (EPL)","cited_by":122,"doi":"10.1209/epl/i2003-00143-x","arxiv_id":"cond-mat/0211084","nct_id":null,"source_url":"https://arxiv.org/abs/cond-mat/0211084","archive_url":"https://space-kitty.com/papers/arxiv:cond-mat/0211084","updated":"2002-11-05","pmid":null,"pmcid":null,"openalex_id":"W2014463248","pdf_url":"https://arxiv.org/pdf/cond-mat/0211084","abstract":"We present a first principles investigation of the lattice dynamics and electron-phonon coupling of the high-T_c superconductor YBa_2Cu_3O_7 within the framework of density functional perturbation theory using a mixed-basis pseudopotential method. The calculated phonon dispersion curves are in excellent agreement with Raman, infrared and neutron data. Calculation of the Eliashberg function alpha^2F leads to a small electron-phonon coupling lambda=0.27 in disagreement with earlier approximate treatments. Our calculations strongly support the view that conventional electron-phonon coupling is not an important contribution to superconductivity in high-T_c materials.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1007/s00521-021-06304-z","kind":"article","title":"Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques","authors":["Paulino José García Nieto","Esperanza García–Gonzalo","José P. Paredes–Sánchez"],"author_count":3,"year":2021,"published":"2021-07-20","venue":"Neural Computing and Applications","cited_by":66,"doi":"10.1007/s00521-021-06304-z","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s00521-021-06304-z","archive_url":"https://space-kitty.com/papers/doi:10.1007/s00521-021-06304-z","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3183906975","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00521-021-06304-z.pdf","abstract":"Abstract This study builds a predictive model capable of estimating the critical temperature of a superconductor from experimentally determined physico-chemical properties of the material (input variables): features extracted from the thermal conductivity, atomic radius, valence, electron affinity and atomic mass. This original model is built using a novel hybrid algorithm relied on the multivariate adaptive regression splines (MARS) technique in combination with a nature-inspired meta-heuristic optimization algorithm termed the whale optimization algorithm (WOA) that mimics the social behavior of humpback whales. Additionally, the Ridge, Lasso and Elastic-net regression models were fitted to the same experimental data for comparison purposes. The results of the current investigation indicate that the critical temperature of a superconductor can be successfully predicted using this proposed hybrid WOA/MARS-based model. Furthermore, the results obtained with the Ridge, Lasso and Elastic-net regression models are clearly worse than those obtained with the WOA/MARS-based model.","abstract_license":"CC-BY (publisher license, via OpenAlex)","license":"cc-by","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Abstract of \"Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques\" by Paulino José García Nieto, Esperanza García–Gonzalo, José P. Paredes–Sánchez, https://doi.org/10.1007/s00521-021-06304-z, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified."},{"id":"arxiv:1905.06780","kind":"article","title":"Functional form of the superconducting critical temperature from machine learning","authors":["Stephen Xie","Gregory R. Stewart","J. J. Hamlin","P. J. Hirschfeld","Richard G. Hennig"],"author_count":5,"year":2019,"published":"2019-11-18","venue":"Physical review. B./Physical review. B","cited_by":68,"doi":"10.1103/physrevb.100.174513","arxiv_id":"1905.06780","nct_id":null,"source_url":"https://doi.org/10.1103/physrevb.100.174513","archive_url":"https://space-kitty.com/papers/arxiv:1905.06780","updated":"2019-05-16","pmid":null,"pmcid":null,"openalex_id":"W2945348943","pdf_url":"https://arxiv.org/pdf/1905.06780","abstract":"Predicting the critical temperature $T_c$ of new superconductors is a notoriously difficult task, even for electron-phonon paired superconductors for which the theory is relatively well understood. Early attempts by McMillan and Allen and Dynes to improve on the weak-coupling BCS formula led to closed-form approximate relations between $T_c$ and various measures of the phonon spectrum and the electron-phonon interaction appearing in Eliashberg theory. Here we propose that these approaches can be improved with the use of machine learning algorithms. As an initial test, we train a model for identifying low-dimensional descriptors using the $T_c < 10$ K data tested by Allen and Dynes, and show that a simple analytical expression thus obtained improves upon the Allen-Dynes fit. Furthermore, the prediction for the recently discovered high $T_c$ material H$_3$S at high pressure is quite reasonable. Interestingly, $T_c$'s for more recently discovered superconducting systems with a more two-dimensional electron-phonon coupling, which do not follow Allen and Dynes' expression, also do not follow our analytic expression. Thus, this machine learning approach appears to be a powerful method for highlighting the need for a new descriptor beyond those used by Allen and Dynes to describe their set of isotropic electron-phonon coupled superconductors. We argue that this machine learning method, and its implied need for a descriptor characterizing Fermi surface properties, represents a promising new approach to superconductor materials discovery which may eventually replace the serendipitous discovery paradigm begun by Kamerlingh Onnes.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1021/acs.jpcc.2c01904","kind":"article","title":"Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor","authors":["Jingzi Zhang","Zhuoxuan Zhu","X.‐D. Xiang","Ke Zhang","Shangchao Huang","Chengquan Zhong","Hua‐Jun Qiu","Kailong Hu","Xi Lin"],"author_count":9,"year":2022,"published":"2022-05-12","venue":"The Journal of Physical Chemistry C","cited_by":55,"doi":"10.1021/acs.jpcc.2c01904","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1021/acs.jpcc.2c01904","archive_url":"https://space-kitty.com/papers/doi:10.1021/acs.jpcc.2c01904","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4280512145","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"doi:10.1007/s10909-020-02545-9","kind":"article","title":"Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process Regression","authors":["Yun Zhang","Xiaojie Xu"],"author_count":2,"year":2020,"published":"2020-11-10","venue":"Journal of Low Temperature Physics","cited_by":56,"doi":"10.1007/s10909-020-02545-9","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1007/s10909-020-02545-9","archive_url":"https://space-kitty.com/papers/doi:10.1007/s10909-020-02545-9","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W3102656105","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1105.2833","kind":"article","title":"Quantitative reliability of Migdal-Eliashberg theory for strong electron-phonon coupling","authors":["Johannes Bauer","Jong E. Han","Olle Gunnarsson"],"author_count":3,"year":2011,"published":"2011-05-13","venue":"Phys. Rev. B 84 (2011), 184531","cited_by":66,"doi":"10.1103/physrevb.84.184531","arxiv_id":"1105.2833","nct_id":null,"source_url":"https://arxiv.org/abs/1105.2833","archive_url":"https://space-kitty.com/papers/arxiv:1105.2833","updated":"2011-11-30","pmid":null,"pmcid":null,"openalex_id":"W1847624119","pdf_url":"https://arxiv.org/pdf/1105.2833","abstract":"We reassess the validity of Migdal-Eliashberg (ME) theory for coupled electron-phonon systems for large couplings $λ$. Although model calculations have found that ME theory breaks down for $λ\\sim 0.5$, it is routinely applied for $λ>1$ to strong coupling superconductors. To resolve this discrepancy it is important to distinguish between {\\em bare} parameters, used as input in models, and {\\em effective} parameters, derived from experiments. We show explicitly that ME gives accurate results for the critical temperature and the spectral gap for large effective $λ$. This provides quantitative theoretical support for the applicability of ME theory to strong coupling conventional superconductors.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con","cond-mat.str-el"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:0902.1636","kind":"article","title":"Electron-Phonon Coupling in High-Temperature Cuprate Superconductors Determined from Electron Relaxation Rates","authors":["C. Gadermaier","A. S. Alexandrov","V. V. Kabanov","P. Kusar","T. Mertelj","X. Yao","C. Manzoni","D. Brida","G. Cerullo","D. Mihailovic"],"author_count":10,"year":2009,"published":"2009-02-10","venue":"C. Gadermaier et al. Phys. Rev. Lett. 105, 257001 (2010)","cited_by":172,"doi":"10.1103/physrevlett.105.257001","arxiv_id":"0902.1636","nct_id":null,"source_url":"https://arxiv.org/abs/0902.1636","archive_url":"https://space-kitty.com/papers/arxiv:0902.1636","updated":"2010-12-15","pmid":null,"pmcid":null,"openalex_id":"W2023684208","pdf_url":"https://arxiv.org/pdf/0902.1636","abstract":"We determined electronic relaxation times via pump-probe optical spectroscopy using sub-15 fs pulses for the normal state of two different cuprate superconductors.We show that the primary relaxation process is the electron-phonon interaction and extract a measure of its strength, the second moment of the Eliashberg functionλ<ω^{2}>=800\\pm200 meV^{2} for La_{1.85}Sr_{0.15}CuO_{4} and λ<ω^{2}>=400\\pm100 meV^{2} for YBa_{2}Cu_{3}O_{6.5}. These values suggest a possible fundamental role of the electron-phonon interaction in the superconducting pairing mechanism.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:1708.08870","kind":"article","title":"Electron-phonon coupling mechanisms for hydrogen-rich metals at high pressure","authors":["K. Tanaka","J. S. Tse","H. Liu"],"author_count":3,"year":2017,"published":"2017-08-29","venue":"Physical Review B 96, 100502(R) (2017)","cited_by":85,"doi":"10.1103/physrevb.96.100502","arxiv_id":"1708.08870","nct_id":null,"source_url":"https://arxiv.org/abs/1708.08870","archive_url":"https://space-kitty.com/papers/arxiv:1708.08870","updated":"2017-09-27","pmid":null,"pmcid":null,"openalex_id":"W2753633713","pdf_url":"https://arxiv.org/pdf/1708.08870","abstract":"The mechanisms for strong electron-phonon coupling predicted for hydrogen-rich alloys with high superconducting critical temperature ($T_c$) are examined within the Migdal-Eliashberg theory. Analysis of the functional derivative of $T_c$ with respect to the electron-phonon spectral function shows that at low pressures, when the alloys often adopt layered structures, bending vibrations have the most dominant effect. At very high pressures, the H-H interactions in two-dimensional (2D) and three-dimensional (3D) extended structures are weakened, resulting in mixed bent (libration) and stretch vibrations, and the electron-phonon coupling process is distributed over a broad frequency range leading to very high $T_c$.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:1402.0071","kind":"article","title":"Local measurement of the Eliashberg function of Pb islands: enhancement of electron-phonon coupling by quantum well states","authors":["Michael Schackert","Tobias M ärkl","Jasmin Jandke","Martin H ölzer","Sergey Ostanin","Eberhard K. U. Gross","Arthur Ernst","Wulf Wulfhekel"],"author_count":8,"year":2014,"published":"2014-02-01","venue":"Physical Review Letters","cited_by":47,"doi":"10.1103/physrevlett.114.047002","arxiv_id":"1402.0071","nct_id":null,"source_url":"https://arxiv.org/abs/1402.0071","archive_url":"https://space-kitty.com/papers/arxiv:1402.0071","updated":"2014-02-01","pmid":null,"pmcid":null,"openalex_id":"W2059325175","pdf_url":"https://arxiv.org/pdf/1402.0071","abstract":"Inelastic tunneling spectroscopy of Pb islands on Cu(111) obtained by scanning tunneling microscopy below 1K provides a direct access to the local Eliashberg function of the islands with high energy resolution. The Eliashberg function describes the electron-phonon interaction causing conventional superconductivity. The measured Eliashberg function strongly depends on the local thickness of the Pb nanostructures and shows a sharp maximum when quantum well states of the Pb islands come close to the Fermi energy. Ab initio calculations reveal that this is related to enhanced electron-phonon coupling at these thicknesses.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con","cond-mat.mtrl-sci"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:2207.10134","kind":"article","title":"High-throughput screening of strong electron-phonon couplings in ternary metal diborides","authors":["Renhai Wang","Yang Sun","Feng Zhang","Feng Zheng","Yimei Fang","Shunqing Wu","Huafeng Dong","Cai-Zhuang Wang","Vladimir Antropov","Kai-Ming Ho"],"author_count":10,"year":2022,"published":"2022-07-20","venue":"Inorganic Chemistry","cited_by":23,"doi":"10.1021/acs.inorgchem.2c02829","arxiv_id":"2207.10134","nct_id":null,"source_url":"https://arxiv.org/abs/2207.10134","archive_url":"https://space-kitty.com/papers/arxiv:2207.10134","updated":"2022-07-20","pmid":null,"pmcid":null,"openalex_id":"W4308057182","pdf_url":"https://arxiv.org/pdf/2207.10134","abstract":"We perform a high-throughput screening on phonon-mediated superconductivity in ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength higher than that of MgB2. An anti-correlation between energetic stability and electron-phonon coupling strength is identified. We suggest two phases, i.e., Li3ZrB8 and Ca3YB8, to be synthesized, which show reasonable energetic stability and superconducting critical temperature.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con","cond-mat.mtrl-sci"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.7567/1882-0786/ab2922","kind":"article","title":"An acceleration search method of higher T c superconductors by a machine learning algorithm","authors":["Kaname Matsumoto","Tomoya Horide"],"author_count":2,"year":2019,"published":"2019-06-12","venue":"Applied Physics Express","cited_by":54,"doi":"10.7567/1882-0786/ab2922","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.7567/1882-0786/ab2922","archive_url":"https://space-kitty.com/papers/doi:10.7567/1882-0786/ab2922","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W2950738597","pdf_url":"https://iopscience.iop.org/article/10.7567/1882-0786/ab2922/pdf","abstract":null,"abstract_license":null,"license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:1911.05065","kind":"article","title":"Eliashberg Theory: a short review","authors":["F. Marsiglio"],"author_count":1,"year":2019,"published":"2019-11-12","venue":"Annals of Physics 417, 168102-1-23 (2020)","cited_by":163,"doi":"10.1016/j.aop.2020.168102","arxiv_id":"1911.05065","nct_id":null,"source_url":"https://arxiv.org/abs/1911.05065","archive_url":"https://space-kitty.com/papers/arxiv:1911.05065","updated":"2019-11-12","pmid":null,"pmcid":null,"openalex_id":"W2984863965","pdf_url":"https://arxiv.org/pdf/1911.05065","abstract":"Eliashberg theory is a theory of superconductivity that describes the role of phonons in providing the attractive interaction between two electrons. Phonon dynamics are taken into account, thus giving rise to retardation effects that impact the electrons, in the form of a frequency-dependent electron self-energy. In the superconducting state, this means that the order parameter, generally considered to be a static quantity in the Bardeen-Cooper-Schrieffer (BCS) theory, also becomes frequency-dependent. Here we review the finite temperature formulation of Eliashberg theory, both on the imaginary and real frequency axis, and briefly display some examples of the consequences of a dynamical, as opposed to static, interaction. Along the way we point out where further work is required, concerning the validity of some of the assumptions used.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"arxiv:0902.0288","kind":"article","title":"Electron-Phonon Properties of Pnictide Superconductors","authors":["L. Boeri","O. V. Dolgov","A. A. Golubov"],"author_count":3,"year":2009,"published":"2009-02-02","venue":"Physica C Superconductivity","cited_by":43,"doi":"10.1016/j.physc.2009.03.020","arxiv_id":"0902.0288","nct_id":null,"source_url":"https://arxiv.org/abs/0902.0288","archive_url":"https://space-kitty.com/papers/arxiv:0902.0288","updated":"2009-02-02","pmid":null,"pmcid":null,"openalex_id":"W1971605477","pdf_url":"https://arxiv.org/pdf/0902.0288","abstract":"In this paper we discuss the normal and superconducting state properties of two pnictide superconductors, LaOFeAs and LaONiAs, using Migdal-Eliashberg theory and density functional perturbation theory. For pure LaOFeAs, the calculated electron-phonon coupling constant $λ=0.21$ and logarithmic-averaged frequency $ω_{ln}=206 K$, give a maximum $T_c$ of 0.8 K, using the standard Migdal-Eliashberg theory. Inclusion of multiband effects increases the Tc only marginally. To reproduce the experimental $T_c$, a 5-6 times larger coupling constant would be needed. Our results indicate that standard electron-phonon coupling is not sufficient to explain superconductivity in the whole family of Fe-As based superconductors. At the same time, the electron-phonon coupling in Ni-As based compounds is much stronger and its normal and superconducting state properties can be well described by standard Migdal-Eliashberg theory.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["cond-mat.supr-con","cond-mat.str-el"],"details":null,"provenance":"arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."},{"id":"doi:10.1103/physrevb.109.174516","kind":"article","title":"Machine learning accelerated discovery of superconducting two-dimensional Janus transition metal sulfhydrates","authors":["Jingyu Li","Liuming Wei","Xianbiao Shi","Lan‐Ting Shi","Jianguo Si","Pengfei Liu","Bao‐Tian Wang"],"author_count":7,"year":2024,"published":"2024-05-10","venue":"Physical review. B./Physical review. B","cited_by":27,"doi":"10.1103/physrevb.109.174516","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1103/physrevb.109.174516","archive_url":"https://space-kitty.com/papers/doi:10.1103/physrevb.109.174516","updated":null,"pmid":null,"pmcid":null,"openalex_id":"W4396798334","pdf_url":null,"abstract":null,"abstract_license":null,"license":null,"is_open_access":false,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex","attribution":"Metadata and citation counts: OpenAlex (CC0)."},{"id":"arxiv:2002.04977","kind":"article","title":"Critical Temperature Prediction for a Superconductor: A Variational Bayesian Neural Network Approach","authors":["Thanh-Dung Le","Rita Noumeir","Huu Luong Quach","Ji Hyung Kim","Jung Ho Kim","Ho Min Kim"],"author_count":6,"year":2020,"published":"2020-02-04","venue":"IEEE Transactions on Applied Superconductivity","cited_by":41,"doi":"10.1109/tasc.2020.2971456","arxiv_id":"2002.04977","nct_id":null,"source_url":"https://doi.org/10.1109/tasc.2020.2971456","archive_url":"https://space-kitty.com/papers/arxiv:2002.04977","updated":"2020-01-29","pmid":null,"pmcid":null,"openalex_id":"W3005497310","pdf_url":"https://arxiv.org/pdf/2002.04977","abstract":"Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counter-intuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature $T_c$ from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict $T_c$. In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and; then, gives the estimation for the $T_c$. Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance.","abstract_license":"CC0-1.0 (arXiv descriptive metadata)","license":null,"is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":["Machine Learning in Materials Science"],"details":null,"provenance":"openalex+arxiv","attribution":"Abstract: arXiv descriptive metadata (CC0). Thank you to arXiv for use of its open access interoperability. This service was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv."}],"next":"https://space-kitty.com/api/v1/topics/superconductor-discovery/papers?sort=top&limit=25&offset=25"}