As AI technologies continue to evolve, XRD analysis will become increasingly automated, intelligent, and predictive. Alfa Chemistry remains committed to advancing AI-driven analysis and testing technologies through continuous innovation, database expansion, and close collaboration with industrial and research partners worldwide.
Neural Network Architecture & Physics-Informed Training
Intelligent Preprocessing Pipelines
High-Dimensional Search-Match Vectorization
At the heart of the tool is a customized convolutional neural network (CNN) coupled with Transformer blocks, optimized for one-dimensional sequence data. Standard CNNs excel at recognizing localized peak shapes, while Transformer layers capture long-range dependencies. To train this network, we utilized a hybrid data strategy. We fed the model millions of experimental diffractograms harvested from our vast, proprietary testing archives. To ensure absolute coverage of rare phases, we augmented this dataset with synthetically generated patterns calculated directly from structural databases using the Kinematical Theory of X-ray Diffraction.
Raw laboratory data is rarely clean. Our team developed an automated preprocessing pipeline that executes simultaneously upon data ingestion:
Instead of executing linear, peak-by-peak comparisons against a database—a method that slows down exponentially as reference libraries grows—our tool converts both the sample diffractogram and the reference library into low-dimensional, high-density mathematical vectors (embeddings). The search-match process is then executed as a high-speed vector similarity calculation in a multi-dimensional space, reducing database matching times.
