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From individual elements to macroscopic materials: in search of new superconductors via machine learning

Physics

From individual elements to macroscopic materials: in search of new superconductors via machine learning

C. Pereti, K. Bernot, et al.

This groundbreaking research by Claudio Pereti and team introduces a novel supervised classification and regression method using DeepSet technology to predict superconductive materials. The study not only confirms superconductivity in a synthetic analogue of michenerite but also identifies monchetundraite for the first time, matching its critical temperature predictions. A remarkable step forward in AI-assisted material discovery!

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Playback language: English
Abstract
This paper proposes a supervised classification and regression approach for superconductive materials using DeepSet technology. The method successfully predicts both whether a material is superconducting and its critical temperature (Tc). Three materials identified by the neural network were experimentally characterized, confirming superconductivity in a synthetic analogue of michenerite (PdBiTe) and, for the first time, in monchetundraite (Pd₂NiTe₃), with Tc values matching predictions. This is the first superconducting material identified by AI.
Publisher
npj Computational Materials
Published On
May 02, 2023
Authors
Claudio Pereti, Kevin Bernot, Thierry Guizouarn, František Laufek, Anna Vymazalová, Luca Bindi, Roberta Sessoli, Duccio Fanelli
Tags
superconductivity
machine learning
DeepSet technology
material science
critical temperature

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