The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements, as well as the relationships among multiple properties, to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data is available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with Transfer learning (H-CLMP(T)) wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well-suited for multi-target regression across the physical sciences.

Robust automation of analysis procedures capable of handling diverse data sets is critical for high data throughput experiments at the Linac Coherent Light Source (LCLS). One challenge encountered in this process is determining the number of clusters required for the execution of conventional clustering algorithms. It is demonstrated here that bi-cross validation of the inverted and regularized Laplacian, used in the spectral clustering algorithm, yields a robust minimum at the predicted number of clusters and kernel hyper parameters. These results indicate that the process of estimating the number of clusters should not be divorced from the process of estimating other hyper parameters. Applying this method to LCLS xray scattering data demonstrates the ability to identify clusters of dropped shots without manually setting boundaries on detector fluence and provides a path towards identifying rare events.

Due to the stochastic SASE operating principles and other technical issues the output pulses are subject to large fluctuations, making it necessary to characterize the x-ray pulses on every shot for data sorting purposes. We present a technique that applies machine learning tools to predict x-ray pulse properties using simple electron beam and x-ray parameters as input. Using this technique at the Linac Coherent Light Source (LCLS), we report mean errors below 0.3 eV for the prediction of the photon energy at 530 eV and below 1.6 fs for the prediction of the delay between two x-ray pulses. We also demonstrate spectral shape prediction with a mean agreement of 97%. This approach could potentially be used at the next generation of high-repetition-rate XFELs to provide accurate knowledge of complex x-ray pulses at the full repetition rate. I. INTRODUCTION X-ray free-electron lasers (XFELs) 1-3 are emerging as one of the most versatile tools in x-ray research, becoming widely used by the scientific community, as well as industry, in many fields including physics, chemistry, biology, and material science. Their brightness, coherence, tun-ability, and ability to generate pairs of few-fs multicolor pulses for pump-probe experiments 4-7 make them ideal sources to perform diffract-before-destroy imaging 8, resonant x-ray spectroscopy 9, and a range of time resolved measurements of picosecond to few-femtosecond dynamics in molecules and atoms 10-16 . A drawback to XFELs is their current poor stability. XFELs are driven by single-pass electron linear accelerators (LINAC) typically several hundred meters in length.