Computational & AI-Driven Superconductor Discovery
Ab initio Eliashberg predictions, machine-learning Tc models and high-throughput screening for new conventional and unconventional superconductors.
Most influential
- Machine learning modeling of superconducting critical temperature
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 he…
Article499 citationsarXiv:1709.02727doi:10.1038/s41524-018-0085-8 - Evaluating explorative prediction power of machine learning algorithms for materials discovery using k -fold forward cross-validationArticle337 citationsdoi:10.1016/j.commatsci.2019.109203
- EPW: Electron-phonon coupling, transport and superconducting properties using maximally localized Wannier functions
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, lin…
Article1,289 citationsarXiv:1604.03525doi:10.1016/j.cpc.2016.07.028 - Is LaO$_{1-x}$F$_x$FeAs an electron-phonon superconductor ?
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 standar…
Article605 citationsarXiv:0803.2703doi:10.1103/physrevlett.101.026403 - First-Principles Calculation of the Superconducting Transition in MgB2 within the Anisotropic Eliashberg Formalism
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 av…
Article383 citationsarXiv:cond-mat/0111182doi:10.1103/physrevb.66.020513 - Machine Learning F-Doped Bi(Pb)–Sr–Ca–Cu–O Superconducting Transition TemperatureArticle109 citationsdoi:10.1007/s10948-020-05682-0
- Predicting doped Fe-based superconductor critical temperature from structural and topological parameters using machine learningArticle98 citationsdoi:10.1515/ijmr-2020-7986
- Machine learning of superconducting critical temperature from Eliashberg theory
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…
Abstract excerpt by Stephen Xie et al., CC-BY, https://doi.org/10.1038/s41524-021-00666-7; truncated.
Article86 citationsdoi:10.1038/s41524-021-00666-7 - Predicting novel superconducting hydrides using machine learning approaches
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 sup…
Article77 citationsarXiv:2001.09852doi:10.1103/physrevb.101.144505 - Predicting new superconductors and their critical temperatures using machine learning
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 dete…
Article73 citationsarXiv:2002.07266doi:10.1016/j.physc.2020.1353689 - Phonon dispersion and electron-phonon interaction for YBa_2Cu_3O_7 from first-principles calculations
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. Calcul…
Article122 citationsarXiv:cond-mat/0211084doi:10.1209/epl/i2003-00143-x - Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques
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 h…
Abstract excerpt by Paulino José García Nieto et al., CC-BY, https://doi.org/10.1007/s00521-021-06304-z; truncated.
Article66 citationsdoi:10.1007/s00521-021-06304-z - Functional form of the superconducting critical temperature from machine learning
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 meas…
Article68 citationsarXiv:1905.06780doi:10.1103/physrevb.100.174513 - Machine Learning Prediction of Superconducting Critical Temperature through the Structural DescriptorArticle55 citationsdoi:10.1021/acs.jpcc.2c01904
- Fe-Based Superconducting Transition Temperature Modeling through Gaussian Process RegressionArticle56 citationsdoi:10.1007/s10909-020-02545-9
Newest work
- Point-contact spectroscopy of electron-phonon interactions and anisotropy of superconducting energy gaps in $2H-NbS{{e}_{2}}$PreprintarXiv:2610.37304
- Learning the Chronology of Materials Discovery for Synthesizability PredictionPreprintarXiv:2610.36193
- Phonon-Dressed Quasiparticles in Epitaxial Scandium Nitride Resolved by Soft X-ray ARPESPreprintarXiv:2610.33533
- Flexo-Photoresponses from First PrinciplesPreprintarXiv:2610.32341
- Interplay of Chemical Disorder, Vortex Pinning, and Electron Correlations in the Mechanically Robust Medium-Entropy Alloy Superconductor $\text{Nb}_{0.45}\text{Ta}_{0.25}\text{Ti}_{0.15}\text{Hf}_{0.15}$PreprintarXiv:2610.32449
- Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction PatternsPreprintarXiv:2610.33310
- Chiroptical Valleytronic in SnSePreprintarXiv:2610.30930
- Nonadiabatic vibrational dynamics of CO on Pt(111) studied with first-principles many-body perturbation theoryPreprintarXiv:2610.31727
- Symmetry-Based Parameter Efficient Low-Rank Fine-Tuning for High-Throughput Calculation of Lattice Thermal ConductivityPreprintarXiv:2610.29634
- Fully Automated Wannier Disentanglement via Projectability Selection in Gaussian and Augmented Plane-Wave FrameworksPreprintarXiv:2610.27411