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. This work presents a closed-loop machine learning-based strategy for colloidal synthesis of nanoparticles, assuming no prior knowledge of the synthetic process, in order to show that synthetic discovery can be accelerated despite limited data availability.

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. Additionally, descriptors (e.g. grain size) not included in previous reviews were also extracted for articles that reported them. The database is hosted and continually updated on an open data platform, Citrination. To promote interpretation, some data are graphically presented.

Single-Crystal Automated Refinement (SCAR): A Data-Driven Method for Determining Inorganic Structures.

Single-crystal diffraction is one of the most common experimental techniques in chemistry for determining a crystal structure. However, the process of crystal structure determination and refinement is not always straightforward. Methods for simplifying and rationalizing the path to the most optimal crystal structure model have been incorporated into various data processing and crystal structure solution software, with the focus generally on aiding macromolecular or protein structure determination. In this work, we propose a new method that uses single-crystal data to determine the crystal structures of inorganic, extended solids called "single-crystal automated refinement" (SCAR). The approach was developed using data mining and machine learning methods and considers several structural features common in inorganic solids, like atom assignment based on physically reasonable distances, atomic statistical mixing, and crystallographic site deficiency. The output is a tree of possible solutions for the data set with a corresponding fit score indicating the most reasonable crystal structure. Here, the foundation for SCAR is presented followed by the implementation of SCAR to determine two newly synthesized and previously unreported phases, ZrAu0.5Os0.5 and Nd4Mn2AuGe4. The structure solutions are found to be comparable with those produced by manually solving the data set, including the same refined mixed occupancies and atomic deficiency, supporting the validity of this automatic structure solution method. The proposed SCAR program is thus verified as being a fast and reliable assistant in determining even complex single-crystal diffraction data for extended inorganic solids.

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. We compare these potentials to DFT + U derived energetics for a series of xenon defects in a variety of incorporation sites (large, intermediate, and small vacant sites). We find the existing xenon potentials overestimate the energy needed to add a xenon atom to a wide set of defect sites representing a range of incorporation sites, including failing to correctly rank the energetics of the small incorporation site defects (xenon in an interstitial and xenon in a uranium site neighboring uranium in an interstitial). These failures are due to problematic descriptions of Xe-O and/or Xe-U interactions of the previous xenon potentials. These failures are corrected by our newly created xenon potential: our IPR-generated potential gives good agreement with DFT + U calculations to which it was not fitted, such as xenon in an interstitial (small incorporation site) and xenon in a double Schottky defect cluster (large incorporation site). Finally, we note that IPR is very flexible and can be applied to a wide variety of potential forms and materials systems, including metals and EAM 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. Furthermore, many materials of great modern interest, such as batteries and hydrogen storage media, contain light elements such as Li and H that only weakly scatter X-rays. Finally, structural refinements generally require significant human input and intuition, as they rely on good initial guesses for the target structure. To address these many challenges, we demonstrate a new hybrid approach, first-principles-assisted structure solution (FPASS), which combines experimental diffraction data, statistical symmetry information and first-principles-based algorithmic optimization to automatically solve crystal structures. We demonstrate the broad utility of FPASS to clarify four important crystal structure debates: the hydrogen storage candidates MgNH and NH(3)BH(3); Li(2)O(2), relevant to Li-air batteries; and high-pressure silane, SiH(4).

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. Surprisingly, disordered substrates with a smoothly varying potential energy landscape are found to encourage edge-on P3HT orientation, while highly ordered substrates have undesirable potential energy wells that reduce the edge-on orientation of P3HT because of substrate-side-chain interactions.