Metal Alloy Hydrogen Storage Screening Using Temperature-Aware ML

The patent replaces traditional computational chemistry methods (ab initio calculations, density functional theory) with machine learning models that use pre-trained neural networks to predict hydrogen storage properties. This substitution dramatically reduces computational time and resource requirements while maintaining prediction accuracy, directly resolving the contradiction between prediction efficiency and system complexity.

The patent creates a virtual library of pre-trained machine learning models that can predict various hydrogen storage properties (storage capacity, kinetics, thermodynamics) without requiring physical experimentation or complex computational simulations for each prediction. This copying approach allows rapid prediction of alloy properties by simply inputting compositional data, improving productivity while keeping the actual prediction system relatively simple.

The patent performs preliminary training of machine learning models using existing experimental data and computational results before actual material screening is needed. These pre-trained models capture the complex relationships between alloy composition and hydrogen storage properties, allowing rapid prediction of new alloy candidates without repeating time-consuming computational simulations or laboratory experiments for each material evaluation.

The patent changes the approach from evaluating each alloy composition through complex multi-parameter calculations to using simplified machine learning predictions that directly output key performance parameters. By training models on compositional data and predicting storage capacity, kinetics, and thermodynamics directly, the system reduces the dimensionality of the screening process while maintaining comprehensive material evaluation quality.