A Comparative Study of Convolutional Neural Networks and Vision Transformers for Quantitative Phase Analysis from X-ray Diffraction

Reliable quantitative phase analysis (QPA) of mineral residues—such as bottom ash from waste incineration and metallurgical slags—is essential for resource recovery and process control, yet traditional methods such as Rietveld refinement are labor-intensive and sensitive to user expertise. Deep learning offers a route to automate XRD-based QPA, but prior architectural comparisons of convolutional neural networks (CNNs) and Vision Transformers (ViTs) have treated phase classification and fraction regression as separate stages; a joint multi-task formulation with a direct, controlled architectural comparison has not been reported. We train a single multi-task model that simultaneously classifies phase presence and regresses phase fractions on mixed-phase XRD patterns, and compare a 1D-CNN against a ViT under identical conditions using a large simulated dataset spanning waste-relevant crystalline phases, together with a small set of real laboratory measurements. Four evaluation scenarios—in-domain simulated, in-domain real, zero-shot transfer, and transfer learning with fine-tuning—explicitly quantify the simulation-to-real domain gap. Both architectures achieve near-perfect classification and regression on simulated data. On real measurements, the ViT outperforms the CNN overall, reducing RMSE by 31% and MAE by 35%, while the CNN retains an advantage for phases with sharp, well-resolved peaks and transfers more robustly in the zero-shot setting. Fine-tuning on a small set of real labeled measurements largely closes the domain gap for both architectures, confirming the practical viability of the simulation-to-real transfer pipeline for automated mineral QPA.