FIND: a forward-inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning

High-performance solid-state hydrogen storage alloys are among the key factors enabling the widespread application of hydrogen energy. However, current materials still face challenges such as limited hydrogen storage capacity and excessive thermodynamic stability, which urgently need to be addressed. In this work, we constructed a large-scale solid-state hydride database, encompassing over 1,000 alloy systems and more than 6,000 valid data records. By integrating alloying strategies with machine learning techniques, the Magpie tool was utilized for feature generation, and a multi-objective regression model was developed to simultaneously predict absorption/desorption plateau pressure, enthalpy change, entropy change, and maximum hydrogen storage capacity using various machine learning algorithms. Furthermore, we achieved the inverse design of solid-state hydrogen storage materials using a variational autoencoder (VAE). By integrating the forward prediction and inverse design models, we developed a forward–inverse navigation and discovery platform for hydrogen storage alloys powered by data-driven machine learning: FIND. The forward module enables rapid prediction of absorption and desorption properties based on alloy composition and testing temperature. Building upon this, an advanced function allows fast prediction for multi-component systems with flexible molar ratios. Subsequently, the inverse module facilitates the screening of potential alloy candidates based on user-defined target properties. Finally, the predictive models were integrated with a genetic algorithm to optimize alloy compositions within the Mg-Ni-La-Ce and Mg-Ni-La systems. Multiple novel high-performance alloy candidates were identified, providing a powerful tool and methodological foundation for high-throughput screening and intelligent development of hydrogen storage materials.