The discovery of high-Tc conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous experimental search. This paves the way to an ever-increasing use of data-driven approaches to the study and design of superconductors. In this work, we propose a new adaptive method to generate meaningful datasets of superconductors, based on element substitution into a small set of representative structural templates, generated by crystal structure prediction methods-adapted high-throughput approach. Our approach realizes an optimal compromise between structural variety and computational efficiency and can be easily generalized to other elements and compositions. As a first application, we apply it to binary hydrides at high pressure, realizing a database of 880 hypothetical structures, characterized with a set of electronic, vibrational, and chemical descriptors. In our Superhydra Database, 139 structures are superconducting according to the McMillan-Allen-Dynes approximation. Studying the distribution of Tc and other properties across the database with advanced statistical and visualization techniques, we are able to obtain comprehensive material maps of the phase space of binary hydrides. The Superhydra database can be thought as a first step of a generalized effort to map conventional superconductivity.

Mapping superconductivity in high-pressure hydrides: The Superhydra project / Saha, Santanu; DI CATALDO, Simone; Giannessi, Federico; Cucciari, Alessio; von der Linden, Wolfgang; Boeri, Lilia. - In: PHYSICAL REVIEW MATERIALS. - ISSN 2475-9953. - 7:5(2022).

Mapping superconductivity in high-pressure hydrides: The Superhydra project

Simone Di Cataldo;Federico Giannessi;Alessio Cucciari;Lilia Boeri
2022

Abstract

The discovery of high-Tc conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous experimental search. This paves the way to an ever-increasing use of data-driven approaches to the study and design of superconductors. In this work, we propose a new adaptive method to generate meaningful datasets of superconductors, based on element substitution into a small set of representative structural templates, generated by crystal structure prediction methods-adapted high-throughput approach. Our approach realizes an optimal compromise between structural variety and computational efficiency and can be easily generalized to other elements and compositions. As a first application, we apply it to binary hydrides at high pressure, realizing a database of 880 hypothetical structures, characterized with a set of electronic, vibrational, and chemical descriptors. In our Superhydra Database, 139 structures are superconducting according to the McMillan-Allen-Dynes approximation. Studying the distribution of Tc and other properties across the database with advanced statistical and visualization techniques, we are able to obtain comprehensive material maps of the phase space of binary hydrides. The Superhydra database can be thought as a first step of a generalized effort to map conventional superconductivity.
2022
superconductivity; machine learning; hydrides; high pressures
01 Pubblicazione su rivista::01a Articolo in rivista
Mapping superconductivity in high-pressure hydrides: The Superhydra project / Saha, Santanu; DI CATALDO, Simone; Giannessi, Federico; Cucciari, Alessio; von der Linden, Wolfgang; Boeri, Lilia. - In: PHYSICAL REVIEW MATERIALS. - ISSN 2475-9953. - 7:5(2022).
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1693933
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 3
social impact