Related papers: DASP: Defect and Dopant ab-initio Simulation Packa…
The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known…
Point defects are responsible for a wide range of optoelectronic properties in materials, making it crucial to engineer their concentrations for novel materials design. However, considering the plethora of defects in co-doped semiconducting…
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and…
We propose the use of incomplete dot products (IDP) to dynamically adjust the number of input channels used in each layer of a convolutional neural network during feedforward inference. IDP adds monotonically non-increasing coefficients,…
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems with ab initio accuracy. Still, the community awaits universal…
The software package DIALECT is introduced, which provides the capability of calculating excited-state properties and nonadiabatic dynamics of large molecular systems and can be applied to simulate energy and charge-transfer processes in…
Recently, we developed a method to construct polynomial interatomic potentials from ab-initio calculations in order to accurately describe laser excited solids [PRL 124, 085501 (2020)]. However, ab-initio methods, and therefore analytical…
The enhanced degradation of organophosphorous-based chemical warfare agents (CWAs) on metal-oxide surfaces holds immense promise for neutralization efforts; however, the underlying mechanisms in this process remain poorly understood. We…
We present an updated version of the AFMPB package for fast calculation of molecular solvation-free energy. The main feature of the new version is the successful adoption of the DASHMM library, which enables AFMPB to operate on distributed…
In this paper, we consider an extended concept of invariant for polynomial dynamical system (PDS) with domain and initial condition, and establish a sound and complete criterion for checking semi-algebraic invariants (SAI) for such PDSs.…
CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular and biological systems. It is especially aimed at massively-parallel and linear-scaling…
The control over material properties attainable through molecular doping is essential to many technological applications of organic semiconductors, such as OLED or thermoelectrics. These excitonic semiconductors typically reach the…
Accelerated degradation testing (ADT) is one of the major approaches in reliability engineering which allows accurate estimation of reliability characteristics of highly reliable systems within a relatively short time. The testing data are…
A new computational method is herein discussed to systemize the development of new dielectric capacitor designs. The method predicts the identities and amounts of (1) gaseous products of decomposition, (2) the volume of the emerged solid…
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…
In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback…
The size and morphology of defect clusters formed during primary damage play a crucial role in the subsequent microstructural evolution of irradiated materials. Molecular dynamics (MD) simulations of collision cascades in tungsten (W) were…
Abstract: In our paper the new algorithm enhanced multi gradient Dilution Preparation (EMDP) is discussed. This new algorithm is reported with a lab on chip or digital Microfluidic biochip to operate multiple operation on a tiny chip. We…
Point defects critically influence the properties of materials and devices, yet density functional theory (DFT) remains computationally demanding for defect supercell calculations. Machine learning interatomic potentials (MLIPs) offer high…
We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio…