Related papers: Catalyst design using actively learned machine wit…
The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some…
Coronary artery disease (CAD) is a leading cause of cardiovascular-related mortality, and accurate stenosis detection is crucial for effective clinical decision-making. Coronary angiography remains the gold standard for diagnosing CAD, but…
Early detection and diagnosis of coronary artery disease (CAD) could save lives and reduce healthcare costs. The current clinical practice is to perform CAD diagnosis through analysing medical images from computed tomography coronary…
Infrared reflection absorption spectroscopy (IRAS) offers a powerful route to bridging the materials and pressure gaps between surface science and powder catalysis. Using a newly developed IRAS setup optimised for dielectric single…
The vibrational frequency of carbon monoxide (CO) adsorbed on ceria-based catalysts serves as a sensitive probe for identifying exposed surface facets, provided that experimental reference data on well-defined single-crystal surfaces and…
One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, more than current model architectures and processing…
Ceria (CeO2) is a promising catalyst for the reduction of carbon dioxide (CO2) to liquid fuels and commodity chemicals, in part because of its high oxygen storage capacity, yet the fundamentals of CO2 adsorption and initial activation on…
In the machine learning domain, active learning is an iterative data selection algorithm for maximizing information acquisition and improving model performance with limited training samples. It is very useful, especially for the industrial…
Colloidoscope is a deep learning pipeline employing a 3D residual Unet architecture, designed to enhance the tracking of dense colloidal suspensions through confocal microscopy. This methodology uses a simulated training dataset that…
Sensitive and accurate diagnostic technologies with magnetic sensors are of great importance for identifying and localizing defects of rechargeable solid batteries in a noninvasive detection. We demonstrate a microwave-free AC magnetometry…
We perform first-principles calculations to investigate the band structure, density of states, optical absorption, and the imaginary part of dielectric function of Cu, Ag, and Au-doped anatase TiO2 in 72 atoms systems. The electronic…
Non-adiabaticity in adsorption on metal surfaces gives rise to a number of measurable effects, such as chemicurrents and exo-electron emission. Here we present a quantitative theory of chemicurrents on the basis of ground-state…
We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are…
In this paper I give a detailed account of an ab initio methodology for describing strong electronic correlations in nanoscale devices hosting transition metal atoms with open $d$- or $f$-shells. The method combines Kohn-Sham Density…
Electrochemical CO2 reduction reaction (CO2RR) using 2D nanomaterials has emerged as a sophisticated approach to mitigate industrial CO2 emissions. In this work, the potential application of pristine as well as strategically Fe, Co,…
A new steady-state kinetic model of ammonia decomposition is presented and analyzed regarding the electronic properties of metal catalysts. The model is based on the classical Temkin-Ertl mechanism and modified in accordance with…
An efficient method for computing the Landauer-Buettiker conductance of an open quantum system within DFT+U is presented. The Hubbard potential is included in electronic structure and transport calculations as a simple renormalization of…
Classical density functional theory (cDFT) provides a systematic approach to predict the structure and thermodynamic properties of chemical systems through the single-molecule density profiles. Whereas the statistical-mechanical framework…
A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with tens to hundreds of atoms this can be solved by using active…
An appropriate model Hamiltonian based formalism is proposed for a random adsorbate layer with arbitrary coverage and the ensuing two-dimensional band formation by metallic adsorbates in the monolayer regime. The coherent potential…