Machine-learning approach for discovery of conventional superconductors

Abstract

First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future discoveries if their reliability can be improved. The main challenge of current ML approaches, typically aiming at predicting the critical temperature $T_{\mathrm{c}}$ of a solid from its chemical composition and target pressure, is that the correlations to be learned are deeply hidden, indirect, and uncertain. In this work, we showed that predicting superconductivity at any pressure from the atomic structure is sustainable and reliable. For a demonstration, we curated a diverse dataset of 584 atomic structures for which $\lambda$ and ${\omega }_{\log }$, two parameters of the electron-phonon interactions, were computed. We then trained some ML models to predict $\lambda$ and ${\omega }_{\log }$, from which $T_{\mathrm{c}}$ can be computed in a post-processing manner. The models were validated and used to identify two possible superconductors whose $T_{\mathrm{c}}\simeq 10-15$K at zero pressure. Interestingly, these materials have been synthesized and studied in some other contexts. In summary, the proposed ML approach enables a pathway to directly transfer what can be learned from the high-pressure atomic-level details that correlate with high-$T_{\mathrm{c}}$ superconductivity to zero pressure. Going forward, this strategy will be improved to better contribute to the discoveries of new superconductors.

References in corpus (15)

• Quantum ESPRESSO: a modular and open-source software project for quantum simulations of materials
• Advanced capabilities for materials modelling with Quantum ESPRESSO
• Recent Advances and Applications of Deep Learning Methods in Materials Science
• Pathways Towards Ferroelectricity in Hafnia
• Design Principles for High Temperature Superconductors with Hydrogen-based Alloy Backbone at Moderate Pressure
• Towards Direct-Gap Silicon Phases by the Inverse Band Structure Design Approach
• Accelerated materials property predictions and design using motif-based fingerprints
• High-temperature superconductivity in hydrides: experimental evidence and details
• Nonstandard superconductivity or no superconductivity in hydrides under high pressure
• Room Temperature Superconductivity: the Roles of Theory and Materials Design
• Tuning Chemical Precompression: Theoretical Design and Crystal Chemistry of Novel Hydrides in the Quest for Warm and Light Superconductivity at Ambient Pressures
• Designing High-Tc Superconductors with BCS-inspired Screening, Density Functional Theory and Deep-learning
• Phase Diagram and Superconductivity of Calcium Borohyrides at Extreme Pressures
• Missing theoretical evidence for conventional room temperature superconductivity in low enthalpy structures of carbonaceous sulfur hydrides
• Probabilistic deep learning approach for targeted hybrid organic-inorganic perovskites

Cited by in corpus (8)

• Feasible route to high-temperature ambient-pressure hydride superconductivity
• The Maximum $T_c$ of Conventional Superconductors at Ambient Pressure
• Charting the landscape of Bardeen-Cooper-Schrieffer superconductors in experimentally known compounds
• Superconductor discovery in the emerging paradigm of Materials Informatics
• Semi-automatic staging area for high-quality structured data extraction from scientific literature
• Machine-learning Guided Search for Phonon-mediated Superconductivity in Boron and Carbon Compounds
• Evidence of Molecular Hydrogen in the N-doped LuH3 System: a Possible Path to Superconductivity?
• Tree Models Machine Learning to Identify Liquid Metal based Alloy Superconductor