Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning

Abstract

We develop a multi-step workflow for the discovery of conventional superconductors, starting with a Bardeen Cooper Schrieffer inspired pre-screening of 1736 materials with high Debye temperature and electronic density of states. Next, we perform electron-phonon coupling calculations for 1058 of them to establish a large and systematic database of BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we identify 105 dynamically stable materials with transition temperatures, Tc>5 K. Additionally, we analyze trends in our dataset and individual materials including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. We demonstrate that deep-learning(DL) models can predict superconductor properties faster than direct first principles computations. Notably, we find that by predicting the Eliashberg function as an intermediate quantity, we can improve model performance versus a direct DL prediction of Tc. We apply the trained models on the crystallographic open database and pre-screen candidates for further DFT calculations.

References in corpus (49)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• Conventional superconductivity at 203 K at high pressures
• SchNet - a deep learning architecture for molecules and materials
• Quantum ESPRESSO toward the exascale
• Pseudopotentials for high-throughput DFT calculations
• Topological superconductors: a review
• Electron-phonon interactions from first principles
• Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
• EPW: Electron-phonon coupling, transport and superconducting properties using maximally localized Wannier functions
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Atomistic Line Graph Neural Network for Improved Materials Property Predictions
• The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
• Machine learning modeling of superconducting critical temperature
• Anisotropic Migdal-Eliashberg theory using Wannier functions
• Iron-Based Superconductors: current status of materials and pairing mechanism
• Ab-initio theory of superconductivity - I: Density functional formalism and approximate functionals
• Ab-initio theory of superconductivity - II: Applications to elemental metals
• Superconducting hydrides under pressure
• New Superconducting and Semiconducting Fe-B Compounds Predicted with an Ab Initio Evolutionary Search
• Convergence and machine learning predictions of Monkhorst-Pack k-points and plane-wave cut-off in high-throughput DFT calculations
• Machine learning with force-field inspired descriptors for materials: fast screening and mapping energy landscape
• Accelerated Discovery of Efficient Solar-cell Materials using Quantum and Machine-learning Methods
• Improved tetrahedron method for the Brillouin-zone integration applicable to response functions
• Computational screening of high-performance optoelectronic materials using OptB88vdW and TB-mBJ formalisms
• High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses
• Elastic properties of bulk and low-dimensional materials using Van der Waals density functional
• Superconductivity of $La{H}_{10}$ and $La{H}_{16}$ polyhydrides
• Superconductivity in the A15 Structure
• High-throughput discovery of high-temperature conventional superconductors
• Strong Pressure Dependent Electron-Phonon Coupling in FeSe
• Superconductivity in the elements, alloys and simple compounds
• Predicting novel superconducting hydrides using machine learning approaches
• Prediction of superconducting properties of CaB2 using anisotropic Eliashberg theory
• Benchmark of density functional theory for superconductors in elemental materials
• Computational Search for Magnetic and Non-magnetic 2D Topological Materials using Unified Spin-orbit Spillage Screening
• First principles search for $n$-type oxide, nitride, and sulfide thermoelectrics
• High-throughput search for magnetic topological materials using spin-orbit spillage, machine-learning and experiments
• Electron-phonon superconductivity in $A$Pt${}_3$P compounds: from weak to strong coupling
• High-throughput Discovery of Topologically Non-trivial Materials using Spin-orbit Spillage
• Stability and superconductivity of lanthanum and yttrium decahydrides
• Crystal Structures and Properties of Iron Hydrides at High Pressure
• Linear and nonlinear low frequency electrodynamics of the surface superconducting states in an yttrium hexaboride a single crystal
• Density Functional Theory based Electric Field Gradient Database
• Recent advances in high-throughput superconductivity research
• Fermi surface in La-based cuprate superconductors from Compton scattering imaging

Cited by in corpus (27)

• AI-driven inverse design of materials: Past, present and future
• High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors
• Inverse Design of Next-generation Superconductors Using Data-driven Deep Generative Models
• Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
• ChemNLP: A Natural Language Processing based Library for Materials Chemistry Text Data
• Recent progress in the JARVIS infrastructure for next-generation data-driven materials design
• Systematic DFT+U and Quantum Monte Carlo benchmark of magnetic two-dimensional (2D) CrX${}_3$ (X = I, Br, Cl, F)
• A Deep-learning Model for Fast Prediction of Vacancy Formation in Diverse Materials
• Machine-learning approach for discovery of conventional superconductors
• InterMat: Accelerating Band Offset Prediction in Semiconductor Interfaces with DFT and Deep Learning
• The Maximum $T_c$ of Conventional Superconductors at Ambient Pressure
• Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning
• InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors
• InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
• Imaginary phonon modes and phonon-mediated superconductivity in Y2C3
• High-throughput screening for boride superconductors
• Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
• Fermionic Quantum Turbulence: Pushing the Limits of High-Performance Computing
• The JARVIS Infrastructure is All You Need for Materials Design
• Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds
• Superconductor discovery in the emerging paradigm of Materials Informatics
• Machine-learning Guided Search for Phonon-mediated Superconductivity in Boron and Carbon Compounds
• Accelerating the Search for Superconductors Using Machine Learning
• Evidence of Molecular Hydrogen in the N-doped LuH3 System: a Possible Path to Superconductivity?
• HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
• Signature of Unconventional Superconductivity in the High Temperature Normal State Resistivity
• Superconducting ternary compounds Li-X-B (X=Mo, W) within the mild pressure range: First-principles predictions