Related papers: PyCDT: A Python toolkit for modeling point defects…
A well-known approach for identifying defect-prone parts of software in order to focus testing is to use different kinds of product metrics such as size or complexity. Although this approach has been evaluated in many contexts, the question…
Efficient use of energy is essential for today's supercomputing systems, as energy cost is generally a major component of their operational cost. Research into "green computing" is needed to reduce the environmental impact of running these…
Accelerated destructive degradation tests (ADDT) are often used to collect necessary data for assessing the long-term properties of polymeric materials. Based on the data, a thermal index (TI) is estimated. The TI can be useful for material…
In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory Tool Kit). DFTTK is based on…
Defect engineering using self-doping or creating vacancies in polycrystalline oxide based materials has profound influence on optical absorption, UV photo detection, and electrical switching. However, defects induced semiconducting oxide…
Functional materials that enable many technological applications in our everyday lives owe their unique properties to defects that are carefully engineered and incorporated into these materials during processing. However, optimizing and…
Density functional approximations (DFAs) suffer from delocalization error, which limits their accuracy in predicting electron affinities (EAs), ionization potentials (IPs), and quasiparticle energies. In this work, we present a theoretical…
Due to their technological importance, point defects in silicon are among the best studied physical systems. The experimental examination of point defects buried in bulk is difficult and evidence for the various defects usually indirect.…
Two dimensional (2D) transition-metal dichalcogenide (TMD) based semiconductors have generated intense recent interest due to their novel optical and electronic properties, and potential for applications. In this work, we characterize the…
The PySCF package has emerged as a powerful and flexible open-source platform for quantum chemistry simulations. However, the efficiency of electronic structure calculations can vary significantly depending on the choice of computational…
PyPOTS is an open-source Python library dedicated to data mining and analysis on multivariate partially-observed time series with missing values. Particularly, it provides easy access to diverse algorithms categorized into five tasks:…
The basic properties of point defects (atomic geometry, the position of charge-transfer levels, and formation energies) on the (110) surface of GaAs, GaP, and InP have been calculated employing density-functional theory. Based on these…
Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change in Gibbs free energy is approximated by the internal…
In materials discovery, the integration of first-principles calculations with machine learning techniques has been actively studied for two key tasks: crystal structure prediction, which searches for stable structures given a chemical…
Point defects in solid-state materials are now routinely simulated using large supercell structures, requiring efficient quantum mechanical solutions. Data-driven and machine learning (ML) models trained on computational data can enable…
Here, we evaluate multislice electron ptychography as a tool to carry out depth-resolved atomic resolution characterization of point defects, using silicon carbide as a case study. Through multislice electron scattering simulations and…
The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high…
Atomically thin two-dimensional (2D) materials are ideal hosts of quantum defects as they offer easier control, manipulation and read-out of defect states compared to bulk systems. Here we introduce the Quantum Point Defect (QPOD) database…
The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of $\beta$-$\rm…
Solid-state point defects are attracting increasing attention in the field of quantum information science, because their localized states can act as a spin-photon interface in devices that store and transfer quantum information, which have…