Batch Discovery of New Metal Superhydrides via Chemical Template Theory and Machine Learning
Yuanhui Sun, Maosheng Miao

TL;DR
This paper introduces a novel machine learning-based method combined with chemical template theory to efficiently discover new metal superhydrides, significantly expanding the known chemical space and identifying promising high-temperature superconductors.
Contribution
The study develops a structure discovery workflow integrating chemical templates and machine learning, leading to the identification of 44 new stable superhydrides and 13 new structural prototypes.
Findings
Identified 31 new stable metal superhydrides, increasing known compounds by 25%.
Discovered 13 new structural prototypes with complex unit cells.
19 superhydrides exhibit superconducting transition temperatures over 100 K.
Abstract
Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Recent research has emphasized "chemical pre-compression," enabling hydrogen metallization at lower pressures and facilitating the discovery of superconductors with near-room temperature transitions. Despite extensive studies on binary metal superhydrides, there remains a vast, unexplored chemical space, particularly regarding non-integer hydrogen-to-metal ratios. By integrating the "chemical template effect" with machine learning algorithms, we developed a specialized structure discovery workflow that significantly enhances the efficiency of predicting stable superhydrides. Our method led to the identification of 13 new structural prototypes and 31 stable metal superhydrides, representing a 25% increase in discoveries. The 3D…
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Taxonomy
TopicsHydrogen Storage and Materials · Superconductivity in MgB2 and Alloys · Rare-earth and actinide compounds
