A diversity-aware selection framework builds materials datasets that improve prediction performance on both targeted (up to 25% gain) and untargeted properties (up to 10% gain) compared to random or non-diverse sampling in noisy experimental settings.
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cond-mat.mtrl-sci 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A proposed physics-aware AI ecosystem combines organized data, physics-based models, and experimental feedback loops to enable consistent optimization and discovery of hydrogen storage materials.
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Building informative materials datasets beyond targeted objectives
A diversity-aware selection framework builds materials datasets that improve prediction performance on both targeted (up to 25% gain) and untargeted properties (up to 10% gain) compared to random or non-diverse sampling in noisy experimental settings.
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Building a physics-aware AI ecosystem for solid-state hydrogen storage materials
A proposed physics-aware AI ecosystem combines organized data, physics-based models, and experimental feedback loops to enable consistent optimization and discovery of hydrogen storage materials.