Related papers: Catalyst design using actively learned machine wit…

We have developed a method that can analyze large random grain boundary (GB) models with the accuracy of density functional theory (DFT) calculations using active learning. It is assumed that the atomic energy is represented by the linear…

A major challenge in creating a quantum computer is to find a quantum system that can be used to implement the qubits. For this purpose, deep centers are prominent candidates, and ab initio calculations are one of the most important tools…

Cardiovascular disease (CVDs) is one of the universal deadly diseases, and the detection of it in the early stage is a challenging task to tackle. Recently, deep learning and convolutional neural networks have been employed widely for the…

The role of catalyst support and regioselectivity of molecular adsorption on a metal oxide surface is investigated for the NO reduction on a Cu/{\gamma}-alumina heterogeneous catalyst. For the solid surface, computational models of the…

The accurate prediction of the electronic properties of materials at a low computational expense is a necessary conditions for the development of effective high-throughput quantum-mechanics (HTQM) frameworks for accelerated materials…

Direct access to transition state energies at low computational cost unlocks the possibility of accelerating catalyst discovery. We show that the top performing graph neural network potential trained on the OC20 dataset, a related but…

We perform a series of calculations using simulated QPUs, accelerated by the NVIDIA CUDA-Q platform, focusing on a molecular analog of an amine-functionalized metal-organic framework (MOF), a promising class of materials for CO$_2$ capture.…

Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using a deep-learning convolutional neural network (CNN). The proof-of-concept automated image acquisition and batch-wise image processing offers the potential for…

Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial…

Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective…

We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited…

Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and…

Extensive density-functional calculations are performed for chemisorption of atoms in the three first periods (H, B, C, N, O, F, Al, Si, P, S, and Cl) on the polar TiC(111) surface. Calculations are also performed for O on TiC(001), for…

We give three new algorithms for efficient in-place estimation, without using ancilla qubits, of average fidelity of a quantum logic gate acting on a d-dimensional system using much fewer random bits than what was known so far. Previous…

Rydberg excitons in the semiconductor Cu$_2$O have been observed in absorption experiments up to a principal quantum number of n = 28 at millikelvin temperatures [1]. Here, we extend the experimental parameter space by variing both…

The adsorption of volatile molecules onto dust grain surfaces fundamentally influences dust-related processes, including condensation of gas-phase molecules, dust coagulation, and planet formation in protoplanetary disks. Using advanced…

The cost of simulating quantum many-body systems - on classical or quantum hardware - scales with the number of variational parameters, so progress at fixed computational budget hinges on more parameter-efficient ans\"atze. Configuration…

The adsorption and desorption of carbon dioxide (CO2) molecule by alkaline earth metal (AEM) (Mg+2, Ca+2, Sr+2 and Ba+2) functionalized on graphitic boron nitride (g-B4N3) nanosheet have been analyzed by using density functional theory…

Machine learning models for 3D molecular property prediction typically rely on atom-based representations, which may overlook subtle physical information. Electron density maps -- the direct output of X-ray crystallography and cryo-electron…

Predicting how organic molecules adsorb, assemble, and interact on metal surfaces is central to surface chemistry and molecular electronics, particularly in the context of interpreting high-resolution scanning probe microscopy. Yet, the…