Automatic hydrogen clathrate recognition neural networks for high-pressure superconductor prediction

Yibo Sun; Yuhang Lu; Zimo Li; Jun Ni

doi:10.1039/d5tc04470g

January 1, 2026Journal of Materials Chemistry C

Automatic hydrogen clathrate recognition neural networks for high-pressure superconductor prediction

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Key Points

The central aim is to develop a machine learning algorithm for predicting high-pressure hydride superconductors.
Proposed a neural network algorithm for clathrate recognition.
Utilized machine learning techniques to assess material properties.
Analyzed data from known high-pressure superconductors.
Achieved high accuracy in predicting potential superconductors.
Successfully identified new hydrogen clathrate structures.
Demonstrated the algorithm's ability to accelerate material discovery.

Implication

Algorithm predicts superconductors in high-pressure hydride materials, indicating advances in material design.

Abstract

Progress continue to be achieved in ultra-high-pressure hydride superconductors recently. Using machine learning to predict and design materials is an important mean of accelerating material development. We propose an algorithm...

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Cite This Study

Sun et al. (2026) studied this question.

synapsesocial.com/papers/698d6e5a5be6419ac0d540c9 https://doi.org/10.1039/d5tc04470g

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Automatic hydrogen clathrate recognition neural networks for high-pressure superconductor prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

Automatic hydrogen clathrate recognition neural networks for high-pressure superconductor prediction

Key Points

Implication

Abstract

Discussion

Cite This Study

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