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.

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.

No abstract

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.

Computation of the van der Waals (vdW) interactions plays a crucial role in the study of layered materials. The adiabatic-connection fluctuation-dissipation theorem within random phase approximation (ACFDT-RPA) has been empirically reported to be the most accurate of commonly used methods, but it is limited to small systems due to its computational complexity. Without a computationally tractable vdW correction, fictitious strains are often introduced in the study of multilayer heterostructures, which, we find, can change the vdW binding energy by as much as 15%. In this work, we employed for the first time a defined Lifshitz model to provide the vdW potentials for a spectrum of layered materials orders of magnitude faster than the ACFDT-RPA for representative layered material structures. We find that a suitably defined Lifshitz model gives the correlation component of the binding energy to within 8-20% of the ACFDT-RPA calculations for a variety of layered heterostructures. Using this fast Lifshitz model, we studied the vdW binding properties of 210 three-layered heterostructures. Our results demonstrate that the three-body vdW effects are generally small (10% of the binding energy) in layered materials for most cases, and that non-negligible second-nearest neighbor layer interaction and three-body effects are observed for only those cases in which the middle layer is atomically thin (e.g. BN or graphene). We find that there is potential for particular combinations of stacked layers to exhibit repulsive three-body van der Waals effects, although these effects are likely to be much smaller than two-body effects.

Machine learning (ML)‐based approaches to battery design are relatively new but demonstrate significant promise for accelerating the timeline for new materials discovery, process optimization, and cell lifetime prediction. Battery modeling represents an interesting and unconventional application area for ML, as datasets are often small but some degree of physical understanding of the underlying processes may exist. This review article provides discussion and analysis of several important and increasingly common questions: how ML‐based battery modeling works, how much data are required, how to judge model performance, and recommendations for building models in the small data regime. This article begins with an introduction to ML in general, highlighting several important concepts for small data applications. Previous ionic conductivity modeling efforts are discussed in depth as a case study to illustrate these modeling concepts. Finally, an overview of modeling efforts in major areas of battery design is provided and several areas for promising future efforts are identified, within the context of typical small data constraints.

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.

The ability to coherently rearrange structures at the atomic scale is among the grand challenges of physical science. Some of the primary obstacles are non-adiabatic increases in energy, such as intramolecular vibrational relaxation (IVR) and electronic excitations. Motivated by recent advances in strong terahertz (THz) pulse generation, we investigate the potential of THz to circumvent these obstacles. Employing TDDFT-Ehrenfest simulations, we discover that strong THz pulses can drive isomerization of the LiNC molecule over barriers greater than 0.2 eV with very low ionization rates and, in the best case, less than 3 meV of residual excess energy. This work points to new potential to predictively manipulate chemical bonds in molecules and materials.