Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons

Predicting crystal structure has always been a challenging problem for physical sciences. Recently, computational methods have been built to predict crystal structure with success but have been limited in scope and computational time. In this paper, we review computational methods such as density functional theory and machine learning methods used to predict crystal structure. We also explored the breadth versus accuracy of building a model to predict across any crystal structure using machine learning. We extracted 24 913 unique chemical formulas existing between 290 and 310 K from the Pearson Crystal Database. Of these 24 913 formulas, there exists 10 711 unique crystal structures referred to as entry prototypes. Common entries might have hundreds of chemical compositions, while the vast majority of entry prototypes is represented by fewer than ten unique compositions. To include all data in our predictions, entry prototypes that lacked a minimum number of representatives were relabeled as “Other”. By selecting the minimum numbers to be 150, 100, 70, 40, 20, and 10, we explored how limiting class sizes affected performance. Using each minimum number to reorganize the data, we looked at the classification performance metrics: accuracy, precision, and recall. Accuracy ranged from 97 ± 2 to 85 ± 2%; average precision ranged from 86 ± 2 to 79 ± 2%, while average recall ranged from 73 ± 2 to 54 ± 2% for minimum-class representatives from 150 to 10, respectively

Solution-Based Carbohydrate Synthesis of Individual Solid, Hollow, and Porous Carbon Nanospheres Using Spray Pyrolysis

A facile and scalable solution-based, spray pyrolysis synthesis technique was used to synthesize individual carbon nanospheres with specific surface area (SSA) up to 1106 m²/g using a novel metal-salt catalyzed reaction. The carbon nanosphere diameters were tunable from 10 nm to several micrometers by varying the precursor concentrations. Solid, hollow, and porous carbon nanospheres were achieved by simply varying the ratio of catalyst and carbon source without using any templates. These hollow carbon nanospheres showed adsorption of to 300 mg of dye per gram of carbon, which is more than 15 times higher than that observed for conventional carbon black particles. When evaluated as supercapacitor electrode materials, specific capacitances of up to 112 F/g at a current density of 0.1 A/g were observed, with no capacitance loss after 20 000 cycles

Stable, Heat-Conducting Phosphor Composites for High-Power Laser Lighting

Solid-state lighting using laser diodes is an exciting new development that requires new phosphor geometries to handle the greater light fluxes involved. The greater flux from the source results in more conversion and therefore more conversion loss in the phosphor, which generates self-heating, surpassing the stability of current encapsulation strategies used for light-emitting diodes, usually based on silicones. Here, we present a rapid method using spark plasma sintering (SPS) for preparing ceramic phosphor composites of the canonical yellow-emitting phosphor Ce-doped yttrium aluminum garnet (Ce:YAG) combined with a chemically compatible and thermally stable oxide, α-Al₂O₃. SPS allows for compositional modulation, and phase fraction, microstructure, and luminescent properties of ceramic composites with varying compositions are studied here in detail. The relationship between density, thermal conductivity, and temperature rise during laser-driven phosphor conversion is elucidated, showing that only modest densities are required to mitigate thermal quenching in phosphor composites. Additionally, the scattering nature of the ceramic composites makes them ideal candidates for laser-driven white lighting in reflection mode, where Lambertian scattering of blue light offers great color uniformity, and a luminous flux >1000 lm is generated using a single commercial laser diode coupled to a single phosphor element