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The crystallization of amorphous solids impacts fields ranging from inorganic crystal growth to biophysics. Promoting or inhibiting nanoscale epitaxial crystallization and selecting its final products underpin applications in cryopreservation, semiconductor devices, oxide electronics, quantum electronics, structural and functional ceramics, and advanced glasses. As precursors for crystallization, amorphous solids are distinguished from liquids and gases by the comparatively long relaxation times for perturbations of the mechanical stress and for variations in composition or bonding. These factors allow experimentally controllable parameters to influence crystallization processes and to drive materials toward specific outcomes. For example, amorphous precursors can be employed to form crystalline phases, such as polymorphs of Al 2 O 3 , VO 2 , and other complex oxides, that are not readily accessible via crystallization from a liquid or through vapor-phase epitaxy. Crystallization of amorphous solids can further be guided to produce a desired polymorph, nanoscale shape, microstructure, or orientation of the resulting crystals. These effects enable advances in applications in electronics, magnetic devices, optics, and catalysis. Directions for the future development of the chemical physics of crystallization from amorphous solids can be drawn from the structurally complex and nonequilibrium atomic arrangements in liquids and the atomic-scale structure of liquid–solid interfaces. 

In this paper, we describe our solution to predict student STEM career choices during the 2017 ASSISTments Datamining Competition. We built a machine learning system that automatically reformats the data set, generates new features and prunes redundant ones, and performs model and feature selection. We designed the system to automatically find a model that optimizes prediction performance, yet the final model is a simple logistic regression that allows researchers to discover important features and study their effects on STEM career choices. We also compared our method to other methods, which revealed that the key to good prediction is proper feature enrichment in the beginning stage of the data analysis, while feature selection in a later stage allows a simpler final model. 

Abstract An array of twelve 0.28 kg lithium molybdate (LMO) low-temperature bolometers equipped with 16 bolometric Ge light detectors, aiming at optimization of detector structure for CROSS and CUPID double-beta decay experiments, was constructed and tested in a low-background pulse-tube-based cryostat at the Canfranc underground laboratory in Spain. Performance of the scintillating bolometers was studied depending on the size of phonon NTD-Ge sensors glued to both LMO and Ge absorbers, shape of the Ge light detectors (circular vs. square, from two suppliers), in different light collection conditions (with and without reflector, with aluminum coated LMO crystal surface). The scintillating bolometer array was operated over 8 months in the low-background conditions that allowed to probe a very low, μBq/kg, level of the LMO crystals radioactive contamination by 228 Th and 226 Ra.