No Arabic abstract
Molecular dynamics simulations play an increasingly important role in the rational design of (nano)-materials and in the study of biomacromolecules. However, generating input files and realistic starting coordinates for these simulations is a major bottleneck, especially for high throughput protocols and for complex multi-component systems. To eliminate this bottleneck, we present the polyply software suite that leverages 1) a multi-scale graph matching algorithm designed to generate parameters quickly and for arbitrarily complex polymeric topologies, and 2) a generic multi-scale random walk protocol capable of setting up complex systems efficiently and independent of the target force-field or model resolution. We benchmark quality and performance of the approach by creating melt simulations of six different polymers using two force-fields with different resolution. We further demonstrate the power of our approach by setting up a multi lamellar microphase-separated block copolymer system for next generation batteries, and by generating a liquid-liquid phase separated polyethylene oxide-dextran system inside a lipid vesicle, featuring both branching and molecular weight distribution of the dextran component.
The MechElastic Python package evaluates the mechanical and elastic properties of bulk and 2D materials using the elastic coefficient matrix ($C_{ij}$) obtained from any ab-initio density-functional theory (DFT) code. The current version of this package reads the output of VASP, ABINIT, and Quantum Espresso codes (but it can be easily generalized to any other DFT code) and performs the appropriate post-processing of elastic constants as per the requirement of the user. This program can also detect the input structures crystal symmetry and test the mechanical stability of all crystal classes using the Born-Huang criteria. Various useful material-specific properties such as elastic moduli, longitudinal and transverse elastic wave velocities, Debye temperature, elastic anisotropy, 2D layer modulus, hardness, Pughs ratio, Cauchys pressure, Kleinman parameter, and Lames coefficients, can be estimated using this program. Another existing feature of this program is to employ the ELATE package [J. Phys.: Condens. Matter 28, 275201 (2016)] and plot the spatial variation of several elastic properties such as Poissons ratio, linear compressibility, shear modulus, and Youngs modulus in three dimensions. Further, the MechElastic package can plot the equation of state (EOS) curves for energy and pressure for a variety of EOS models such as Murnaghan, Birch, Birch-Murnaghan, and Vinet, by reading the inputted energy/pressure versus volume data obtained via numerical calculations or experiments. This package is particularly useful for the high-throughput analysis of elastic and mechanical properties of materials.
Qudi is a general, modular, multi-operating system suite written in Python 3 for controlling laboratory experiments. It provides a structured environment by separating functionality into hardware abstraction, experiment logic and user interface layers. The core feature set comprises a graphical user interface, live data visualization, distributed execution over networks, rapid prototyping via Jupyter notebooks, configuration management, and data recording. Currently, the included modules are focused on confocal microscopy, quantum optics and quantum information experiments, but an expansion into other fields is possible and encouraged. Qudi is available from https://github.com/Ulm-IQO/qudi and is freely useable under the GNU General Public Licence.
The development of science-based categorization strategies for regulatory purposes is not a simple task. It requires understanding the needs and capacity of a wide variety of stakeholders and should consider the potential risks and unintended consequences. For an evolving science area, such as nanotechnologies, the overall uncertainties of designing an effective categorization scheme can be significant. Future nanomaterials may be far more complex and may integrate far different functionalities than modern nanomaterials. There is much that has been learned from our experience with legacy nanomaterials and particulate substances in general. Most of the modern nanomaterials are not new nor dramatically different from parent or existing chemical substances, however there are some nuances. Applying these learnings to define reasonable science-based categories that consider how different emerging nanomaterials might be from existing known substances (while integrating sound concepts as they develop) would be a pragmatic and flexible path forward. However, there are many barriers down this road including a need for improvement and updates to chemical classification systems to improve hazard and risk communications, while promoting transparency and consistency.
Metal nano-aerogels combine a large surface area, a high structural stability, and a high catalytic activity towards a variety of chemical reactions. The performance of such nanostructures is underpinned by the atomic-level distribution of their constituents. Yet monitoring their sub-nanoscale structure and composition to guide property optimization remains extremely challenging. Here, we synthesized Pd nano-aerogels from a K2PdCl4 precursor and two different NaBH4 reductant concentrations in distilled water. Atom probe tomography reveals that the aerogel is poly-crystalline and that impurities (Na, K) are integrated from the solution into grain boundaries. Ab initio calculations indicate that these impurities preferentially bound to the Pd-metal surface and are ultimately found in grain boundaries forming as the particles coalesce during synthesis, with Na atoms thermodynamically equilibrating with the surrounding solution and K atoms remaining between growing grains. If controlled, impurity integration, i.e. grain boundary decoration, may offer opportunities for designing new nano-aerogels.
Structural heterogeneity of amorphous solids present difficult challenges that stymie the prediction of plastic events, which are intimately connected to their mechanical behavior. Based on a perturbation analysis of the potential energy landscape, we derive the atomic nonaffinity as an indicator with intrinsic orientation, which quantifies the contribution of an individual atom to the total nonaffine modulus of the system. We find that the atomic nonaffinity can efficiently characterize the locations of the shear transformation zones, with a predicative capacity comparable to the best indicators. More importantly, the atomic nonaffinity, combining the sign of third order derivative of energy with respect to coordinates, reveals an intrinsic softest shear orientation. By analyzing the angle between this orientation and the shear loading direction, it is possible to predict the protocol-dependent response of plastic events. Employing the new method, the distribution of orientations of shear transformation zones in a model two-dimensional amorphous solids can be measured. The resulting plastic events can be understood from a simple model of independent plastic events occurring at variously oriented shear transformation zones. These results shed light on the characterization and prediction of the mechanical response of amorphous solids.