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.

Xi-cam: a versatile interface for data visualization and analysis.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

Ultrafast electronic and structural response of monolayer MoS2 under intense photoexcitation conditions.

We report on the dynamical response of single layer transition metal dichalcogenide MoS2 to intense above-bandgap photoexcitation using the nonlinear-optical second order susceptibility as a direct probe of the electronic and structural dynamics. Excitation conditions corresponding to the order of one electron-hole pair per unit cell generate unexpected increases in the second harmonic from monolayer films, occurring on few picosecond time-scales. These large amplitude changes recover on tens of picosecond time-scales and are reversible at megahertz repetition rates with no photoinduced change in lattice symmetry observed despite the extreme excitation conditions.

Optical characterization of chemistry in shocked nitromethane with time-dependent density functional theory.

We compute the optical properties of the liquid-phase energetic material nitromethane (CH3NO2) for the first 100 ps behind the front of a simulated shock at 6.5 km/s, close to the experimentally observed detonation shock speed of the material. We utilize molecular dynamics trajectories computed using the multiscale shock technique (MSST) for time-resolved optical spectrum calculations based on both linear response time-dependent DFT (TDDFT) and the Kubo-Greenwood formula with Kohn-Sham DFT wave functions. We find that the TDDFT method predicts an optical conductivity 25-35% lower than the Kubo-Greenwood calculation and provides better agreement with the experimentally measured index of refraction of unreacted nitromethane. We investigate the influence of electronic temperature on the Kubo-Greenwood spectra and find no significant effect at optical wavelengths. In both Kubo-Greenwood and TDDFT, the spectra evolve nonmonotonically in time as shock-induced chemistry takes place. We attribute the time-resolved absorption at optical wavelengths to time-dependent populations of molecular decomposition products, including NO, CNO, CNOH, H2O, and larger molecules. These calculations offer direction for guiding and interpreting ultrafast optical measurements on reactive materials.