While the Discrete Element Method (DEM) provides high-fidelity insights into granular processes like high-energy ball milling, its computational cost can become prohibitive when exploring extensive parameter spaces required for scale-up. This limitation hinders the rapid design and optimization cycles crucial for emerging applications, like mechanochemistry. Surrogate modeling offers a promising path to overcome these computational barriers, yet existing approaches often struggle to accurately represent the complex, moving boundary conditions typical of milling equipment. In this work, we leverage a Signed Distance Function Graph Neural Network (SGN) surrogate tailored to the high-energy, moving-boundary regime of the Emax mill. Trained on DEM data, the SGN jointly predicts particle kinematics for recursive roll-out and a mechanochemistry-relevant global quantity, the global dissipated energy. The model exhibits strong generalization to unseen motion trajectories and moderate jar-shape edits without retraining, while operating with a timestep over 100x larger than required by DEM. In a CPU-only comparison, it achieves a minimum of 6.6× wall-clock speedup. This approach provides a powerful and promising technique for the simulation, analysis, and design optimization of high-energy ball milling equipment.

In this study we investigate the mechanochemical regeneration of sodium borohydride (NaBH₄) from a system comprising hydrated sodium metaborate ( [Formula presented] ) and magnesium hydride (MgH₂). We explore the individual and joint impact of key operational parameters (rotational speed, milling time, ball-to-powder ratio (BPR), and molar ratio) on the regeneration yield. Furthermore, a method for quantifying chemical conversion is introduced relying only on water and thus, offering environmental benefits. This approach additionally facilitates the production and storage of a “ready-to-use” NaBH₄ solution with minimal losses at room temperature. Notably, a yield of 90% is achieved, with a 20% reduction in rotational speed compared to prior literature. This research contributes to sustainable hydrogen storage and presents practical advancements in mechanochemical processes.

High-energy ball milling is a versatile method utilized in mechanochemical reactions and material transformations. Understanding and characterizing the relevant mechanical variables is crucial for the optimization and up-scaling of these processes. To achieve this, the present study delves into differentiating the contributions of normal and tangential interactions during high-energy collisions. Using Discrete Element Method (DEM) simulations, we characterize how operational parameters influence these energy dissipation modes, emphasizing the significance of tangential interactions. Our analysis also reveals how different operational parameters such as ball size, fill ratio, and rotational speed affect the mechanical action inside the milling jar giving rise to multiple operating zones where different modes of energy dissipation can thrive. Finally, we present master curves that generalize findings across a wide range of configurations, offering a tool for characterizing and predicting mechanochemical processes beyond the presented cases. These results provide a robust framework for improving mechanochemical reaction efficiency, and equipment design.

Mechanochemical synthesis faces reproducibility and scale-up challenges due to complex parameter interactions. This study employs machine learning (ML) to predict NaBH4 regeneration yield, integrating chemical experimental data and DEM (Discrete Element Method) derived invariant mechanical descriptors (Ēn, Ēt, fcol/nball). Various algorithms were evaluated, including a two-step modeling strategy to isolate the dominant effect of milling time in our process. Results demonstrate that a two-step Gaussian Process Regression (GPR) model achieves good predictive performance (R2 = 0.83), significantly outperforming single-stage models and providing valuable uncertainty estimates. Tree-based ensembles (XGBoost, RF) also benefit from the two-step approach and can enhance interpretability. This work establishes a framework for using ML to optimize mechanochemical processes, reducing experimental cost and offering a method to link mechanical milling conditions to chemical outcomes, thereby enabling predictive mechanochemistry.