Multi-component background learning automates signal detection for spectroscopic data

Automated experimentation has yielded data acquisition rates that supersede human processing capabilities.Artificial Intelligence offers new possibilities for automating data interpretation to generate large,high-quality datasets.Background subtraction is a long-standing challenge,particularly in settings where multiple sources of the background signal coexist,and automatic extraction of signals of interest from measured signals accelerates data interpretation.Herein,we present an unsupervised probabilistic learning approach that analyzes large data collections to identify multiple background sources and establish the probability that any given data point contains a signal of interest.The approach is demonstrated on X-ray diffraction and Raman spectroscopy data and is suitable to any type of data where the signal of interest is a positive addition to the background signals.While the model can incorporate prior knowledge,it does not require knowledge of the signals since the shapes of the background signals,the noise levels,and the signal of interest are simultaneously learned via a probabilistic matrix factorization framework.Automated identification of interpretable signals by unsupervised probabilistic learning avoids the injection of human bias and expedites signal extraction in large datasets,a transformative capability with many applications in the physical sciences and beyond.

aterials structure-property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Properties can be tailored by tuning composition in high-order composition spaces.For spaces with complex phase behavior,modeling the properties as a function of composition and phase distribution remains a formidable challenge.We present materials structure–property factorization(MSPF)as an approach to automate modeling of such data and identify synergistic phase interactions.MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks(DRNets)to matrix factorization-based modeling of the representative properties of each phase in a dataset.MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation,which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships.Comparing the measured photoactivity to a learned model for non-interacting phases,synergistic phase interactions are identified to guide further photoactivity optimization and understanding.MSPF identifies synergistic interactions of a BiVO_(4)-like phase with both Cu_(2)V_(2)O_(7)-like and CuV_(2)O_(6)-like phases,creating avenues for understanding complex photoelectrocatalysts.