# Computational & AI-Driven Superconductor Discovery Domain: Superconductivity & Quantum Materials · 256 papers indexed · newest 2026-10-29 Ab initio Eliashberg predictions, machine-learning Tc models and high-throughput screening for new conventional and unconventional superconductors. Key concepts: electron-phonon, Eliashberg, machine learning critical temperature, high-throughput, superconductor prediction, density functional theory superconductivity ## Most influential - **[Machine learning modeling of superconducting critical temperature](https://space-kitty.com/papers/arxiv:1709.02727)**. Valentin Stanev, Corey Oses, A. Gilad Kusne et al. · 2018 · npj Computational Materials (arXiv:1709.02727 · doi:10.1038/s41524-018-0085-8 · 499 citations). Source: > 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, … - **[Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validation](https://space-kitty.com/papers/doi:10.1016/j.commatsci.2019.109203)**. Zheng Xiong, Yuxin Cui, Zhonghao Liu et al. · 2019 · Computational Materials Science (doi:10.1016/j.commatsci.2019.109203 · 337 citations). Source: - **[EPW: Electron-phonon coupling, transport and superconducting properties using maximally localized Wannier functions](https://space-kitty.com/papers/arxiv:1604.03525)**. Samuel Poncé, Elena R. Margine, Carla Verdi et al. · 2016 · Computer Physics Communications (arXiv:1604.03525 · doi:10.1016/j.cpc.2016.07.028 · 1,289 citations). Source: > 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. Th… - **[Is LaO$\_{1-x}$F$\_x$FeAs an electron-phonon superconductor ?](https://space-kitty.com/papers/arxiv:0803.2703)**. L. Boeri, O. V. Dolgov, A. A. Golubov · 2008 · Physical Review Letters, 101, 026403 (2008) (arXiv:0803.2703 · doi:10.1103/physrevlett.101.026403 · 605 citations). Source: > 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… - **[First-Principles Calculation of the Superconducting Transition in MgB2 within the Anisotropic Eliashberg Formalism](https://space-kitty.com/papers/arxiv:cond-mat/0111182)**. Hyoung Joon Choi, David Roundy, Hong Sun et al. · 2001 · Phys.Rev.B66:020513,2002 (arXiv:cond-mat/0111182 · doi:10.1103/physrevb.66.020513 · 383 citations). Source: > 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… - **[Machine Learning F-Doped Bi(Pb)–Sr–Ca–Cu–O Superconducting Transition Temperature](https://space-kitty.com/papers/doi:10.1007/s10948-020-05682-0)**. Yun Zhang, Xiaojie Xu · 2020 · Journal of Superconductivity and Novel Magnetism (doi:10.1007/s10948-020-05682-0 · 109 citations). Source: - **[Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learning](https://space-kitty.com/papers/doi:10.1515/ijmr-2020-7986)**. Yun Zhang, Xiaojie Xu · 2021 · International Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) (doi:10.1515/ijmr-2020-7986 · 98 citations). Source: - **[Machine learning of superconducting critical temperature from Eliashberg theory](https://space-kitty.com/papers/doi:10.1038/s41524-021-00666-7)**. Stephen Xie, Yundi Quan, Ajinkya C. Hire et al. · 2022 · npj Computational Materials (doi:10.1038/s41524-021-00666-7 · 86 citations). Source: > 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 re… > _Abstract excerpt by Stephen Xie et al., CC-BY, https://doi.org/10.1038/s41524-021-00666-7; truncated._ - **[Predicting novel superconducting hydrides using machine learning approaches](https://space-kitty.com/papers/arxiv:2001.09852)**. Michael Hutcheon, Alice M. Shipley, Richard J. Needs · 2020 · Physical review. B./Physical review. B (arXiv:2001.09852 · doi:10.1103/physrevb.101.144505 · 77 citations). Source: > 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 $T\_c$.… - **[Predicting new superconductors and their critical temperatures using machine learning](https://space-kitty.com/papers/arxiv:2002.07266)**. Benjamin Roter, Sasa V. Dordevic · 2020 · Physica C Superconductivity (arXiv:2002.07266 · doi:10.1016/j.physc.2020.1353689 · 73 citations). Source: > 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 critic… - **[Phonon dispersion and electron-phonon interaction for YBa\_2Cu\_3O\_7 from first-principles calculations](https://space-kitty.com/papers/arxiv:cond-mat/0211084)**. K. -P. Bohnen, R. Heid, M. Krauss · 2002 · Europhysics Letters (EPL) (arXiv:cond-mat/0211084 · doi:10.1209/epl/i2003-00143-x · 122 citations). Source: > 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 no… - **[Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques](https://space-kitty.com/papers/doi:10.1007/s00521-021-06304-z)**. Paulino José García Nieto, Esperanza García–Gonzalo, José P. Paredes–Sánchez · 2021 · Neural Computing and Applications (doi:10.1007/s00521-021-06304-z · 66 citations). Source: > 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 behavio… > _Abstract excerpt by Paulino José García Nieto et al., CC-BY, https://doi.org/10.1007/s00521-021-06304-z; truncated._ - **[Functional form of the superconducting critical temperature from machine learning](https://space-kitty.com/papers/arxiv:1905.06780)**. Stephen Xie, Gregory R. Stewart, J. J. Hamlin et al. · 2019 · Physical review. B./Physical review. B (arXiv:1905.06780 · doi:10.1103/physrevb.100.174513 · 68 citations). Source: > 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 iden… - **[Machine Learning Prediction of Superconducting Critical Temperature through the Structural Descriptor](https://space-kitty.com/papers/doi:10.1021/acs.jpcc.2c01904)**. Jingzi Zhang, Zhuoxuan Zhu, X.‐D. Xiang et al. · 2022 · The Journal of Physical Chemistry C (doi:10.1021/acs.jpcc.2c01904 · 55 citations). Source: - **[Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process Regression](https://space-kitty.com/papers/doi:10.1007/s10909-020-02545-9)**. Yun Zhang, Xiaojie Xu · 2020 · Journal of Low Temperature Physics (doi:10.1007/s10909-020-02545-9 · 56 citations). Source: ## Newest - **[Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties](https://space-kitty.com/papers/arxiv:2610.39513)**. Ishan Amin, Yue Jian, Nitesh Kumar et al. · 2026 (arXiv:2610.39513). Source: - **[Spin-Orbit Coupling Response Theory within the Oriented Spin Space Group Framework](https://space-kitty.com/papers/arxiv:2610.40149)**. Yuntian Liu, Xiaobing Chen, Yutong Yu et al. · 2026 (arXiv:2610.40149). Source: - **[Point-contact spectroscopy of electron-phonon interactions and anisotropy of superconducting energy gaps in $2H-NbS{{e}\_{2}}$](https://space-kitty.com/papers/arxiv:2610.37304)**. N. L. Bobrov, L. V. Tiutrina, S. B. Feodos'ev et al. · 2026 (arXiv:2610.37304). Source: - **[Parity-Resolved Many-Body Kinetics of a Topological Qubit under Nonequilibrium Phonon Pulses](https://space-kitty.com/papers/arxiv:2610.37793)**. Han Huang · 2026 (arXiv:2610.37793). Source: - **[Interpretable neural equation learning for high-dimensional materials data](https://space-kitty.com/papers/arxiv:2610.37880)**. Philip Jacobson, Prasad Iyer, Remi Dingreville et al. · 2026 (arXiv:2610.37880). Source: - **[Learning the Chronology of Materials Discovery for Synthesizability Prediction](https://space-kitty.com/papers/arxiv:2610.36193)**. Junkil Park, Aron Walsh · 2026 (arXiv:2610.36193). Source: - **[Phonon-Dressed Quasiparticles in Epitaxial Scandium Nitride Resolved by Soft X-ray ARPES](https://space-kitty.com/papers/arxiv:2610.33533)**. Sourav Rudra, Dheemahi Rao, Meng-Jie Huang et al. · 2026 (arXiv:2610.33533). Source: - **[Flexo-Photoresponses from First Principles](https://space-kitty.com/papers/arxiv:2610.32341)**. Zhichao Guo, Hua Wang, Haowei Xu · 2026 (arXiv:2610.32341). Source: - **[Interplay of Chemical Disorder, Vortex Pinning, and Electron Correlations in the Mechanically Robust Medium-Entropy Alloy Superconductor $\\text{Nb}\_{0.45}\\text{Ta}\_{0.25}\\text{Ti}\_{0.15}\\text{Hf}\_{0.15}$](https://space-kitty.com/papers/arxiv:2610.32449)**. Pooja Chourasia, Samiul Sk, Nikita Sharma et al. · 2026 (arXiv:2610.32449). Source: - **[Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns](https://space-kitty.com/papers/arxiv:2610.33310)**. Ming-Chiang Chang, Brian DeCost · 2026 (arXiv:2610.33310). Source: --- Space Kitty Archive · https://space-kitty.com · API: https://space-kitty.com/api/v1 · MCP: https://space-kitty.com/mcp · Premium: $0.20/request via x402 (Base Sepolia (testnet)). 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