Fig. 2: F1-scores achieved by XRD-AutoAnalyzer when applied to simulated patterns in the (top) Li-La-Zr-O and (bottom) Li-Ti-P-O spaces.
From: Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification
a Conventional results (black squares) were obtained on patterns with incrementally improved resolution over 2θ = [10°, 60°], whereas adaptive results (blue circles) were found by resampling a subset of 2θ ⊆ [10°, 60°] with high resolution. b Individual results (black squares) were calculated by analyzing patterns with distinct maxima (2θmax), which were aggregated in a confidence-weighted sum to form the ensemble predictions (green circles). Adaptive results (blue star) were obtained by halting expansion of 2θmax when the prediction confidence exceeded 50%.
