Utilization of machine learning to accelerate colloidal synthesis and discovery.

Machine learning techniques are seeing increased usage for predicting new materials with targeted properties. However, widespread adoption of these techniques is hindered by the relatively greater experimental efforts required to test the predictions. Furthermore, because failed synthesis pathways are rarely communicated, it is difficult to find prior datasets that are sufficient for modeling.

Expanded dataset of mechanical properties and observed phases of multi-principal element alloys.

This data article presents a compilation of mechanical properties of 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this article includes updated records from previous reviews (with minor error corrections) along with new data from articles that were published since 2019. The extracted properties include reported composition, processing method, microstructure, density, hardness, yield strength, ultimate tensile strength (or maximum compression strength), elongation (or maximum compression strain), and Young's modulus.

An improved interatomic potential for xenon in UO2: a combined density functional theory/genetic algorithm approach.

We have created an improved xenon interatomic potential for use with existing UO2 potentials. This potential was fit to density functional theory calculations with the Hubbard U correction (DFT + U) using a genetic algorithm approach called iterative potential refinement (IPR). We examine the defect energetics of the IPR-fitted xenon interatomic potential as well as other, previously published xenon potentials.

A hybrid computational-experimental approach for automated crystal structure solution.

Crystal structure solution from diffraction experiments is one of the most fundamental tasks in materials science, chemistry, physics and geology. Unfortunately, numerous factors render this process labour intensive and error prone. Experimental conditions, such as high pressure or structural metastability, often complicate characterization.

Ordering of poly(3-hexylthiophene) nanocrystallites on the basis of substrate surface energy.

Molecular dynamics simulations are used to study the influence of functionalized substrates on the orientation of poly(3-hexylthiophene) (P3HT) nanocrystallites, which in turn plays a critical role in P3HT-based transistor performance. The effects of alkyl-trichlorosilane self-assembled monolayer packing density, packing order, and end-group functionality are independently investigated. Across these factors, the potential energy surface presented by the substrate to the P3HT molecules is determined to be the main driver of P3HT ordering.