Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userVASPKIT: A User-friendly Interface Facilitating High-throughput Computing and Analysis Using VASP Code
213
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present the VASPKIT, a command-line program that aims at providing a powerful and user-friendly interface to perform high-throughput analysis of a variety of material properties from the raw data produced by the VASP code. It consists of mainly the pre- and post-processing modules. The former module is designed to prepare and manipulate input files such as the necessary input files generation, symmetry analysis, supercell transformation, k-path generation for a given crystal structure. The latter module is designed to extract and analyze the raw data about elastic mechanics, electronic structure, charge density, electrostatic potential, linear optical coefficients, wave function plots in real space, and etc. This program can run conveniently in either interactive user interface or command line mode. The command-line options allow the user to perform high-throughput calculations together with bash scripts. This article gives an overview of the program structure and presents illustrative examples for some of its usages. The program can run on Linux, MacOS, and Windows platforms. The executab
rate research
Read More
Nanotechnology research requires the routine use of characterization methods with high spatial resolution. These experiments are rather costly, not only from the point of view of the expensive microscopes, but also considering the need of a rather specialized equipment operator. Here, we describe the construction of an inexpensive and simple device that allows the analysis of nanoparticle in a FEG-SEM; images can be generated at high magnifications (ex. x500.000) and with nanometric resolution. It is based on the acquisition of transmitted electrons annular dark field (TE-ADF) signal; the systems can carry up to 16 TEM samples and, it is compatible with SEM sample exchange air-lock. Performance test have shown the measured ADF signal showed the atomic number and thickness dependence for transition metal nanoparticle about 10 nm in diameter. Also, the signal quality is high enough that the determination of the histogram of size distribution can be performed using a conventional image processing software, for gold particles in the range of 2-10 nm in diameter. The developed ADF device allows a much faster and cheaper high spatial resolution imaging of nanoparticle samples for routine morphological characterization and, provides an invaluable high throughput tool for an efficient sample screening.
Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. To automate the annotation process for spectroscopic and diffraction data collected in counts of hundreds to thousands, we present a systematic approach compatible with high-performance computing infrastructures using the MapReduce model and task-based parallelization. We implement the approach in software and demonstrate linear computational scaling with respect to spectral components using multidimensional experimental materials characterization datasets from photoemission spectroscopy and powder electron diffraction as benchmarks. Our approach enables efficient generation of high-quality data annotation and online spectral analysis and is applicable to a variety of analytical techniques in materials science and chemistry as a building block for closed-loop experimental systems.
At ambient pressure tin transforms from its ground-state semi-metal $alpha$-Sn (diamond structure) phase to the compact metallic $beta$-Sn phase at 13$^circ$C (286K). There may be a further transition to the simple hexagonal $gamma$-Sn above 450K. These relatively low transition temperatures are due to the small energy differences between the structures, $approx 20$,meV/atom between $alpha$- and $beta$-Sn. This makes tin an exceptionally sensitive test of the accuracy of density functionals and computational methods. Here we use the high-throughput Automatic-FLOW (AFLOW) method to study the energetics of tin in multiple structures using a variety of density functionals. We look at the successes and deficiencies of each functional. As no functional is completely satisfactory, we look Hubbard U corrections and show that the Coulomb interaction can be chosen to predict the correct phase transition temperature. We also discuss the necessity of testing high-throughput calculations for convergence for systems with small energy differences.
Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions. There is increasing competitive desire and regulatory incentive to deploy AI mindfully within financial services. An important mechanism towards that end is to explain AI decisions to various stakeholders. State-of-the-art explainable AI systems mostly serve AI engineers and offer little to no value to business decision makers, customers, and other stakeholders. Towards addressing this gap, in this work we consider the scenario of explaining loan denials. We build the first-of-its-kind dataset that is representative of loan-applicant friendly explanations. We design a novel Generative Adversarial Network (GAN) that can accommodate smaller datasets, to generate user-friendly textual explanations. We demonstrate how our system can also generate explanations serving different purposes: those that help educate the loan applicants, or help them take appropriate action towards a future approval.
Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation, but very few high-quality candidate materials are known. In this work, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from 40000 three-dimensional materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft). First, we screen materials with net magnetic moment > 0.5 {mu}B and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
