相关论文: The PseudoDojo: Training and grading a 85 element …

An arbitrarily reliable quantum computer can be efficiently constructed from noisy components using a recursive simulation procedure, provided that those components fail with probability less than the fault-tolerance threshold. Recent…

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…

With the advancement of Deep Neural Networks (DNN) and large amounts of sensor data from Internet of Things (IoT) systems, the research community has worked to reduce the computational and resource demands of DNN to compute on low-resourced…

In this work, we compare three qubit-mapping strategies to study the structure of the nuclear ground state within the shell model description employing the Variational Quantum Eigensolver (VQE) approach. Although the initial point for…

In this paper, a novel pre- and post-processing algorithm is presented that can double the convergence rate of finite element approximations for linear wave problems. In particular, it is shown that a $q$-step pre- and post-processing…

The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant…

This paper improves and demonstrates the usefulness of the first quantized plane-wave algorithms for the quantum simulation of electronic structure, developed by Babbush et al. and Su et al. We describe the first quantum algorithm for first…

The present work concerns two successful models used today: Point by Point (PbP) and the Monte Carlo approaches. The description of the PbP model and of the extended Los Alamos model for higher energies that takes into account the secondary…

A hybrid approach combining the Tabular Prior-data Fitted Network (TabPFN) with the Coulomb and Proximity Potential Model (CPPM) is developed to investigate $\alpha$-particle preformation factors $P_{\alpha}$ and their impact on…

Constructing atomic models from cryo-electron microscopy (cryo-EM) maps is a crucial yet intricate task in structural biology. While advancements in deep learning, such as convolutional neural networks (CNNs) and graph neural networks…

Consistently predicting biopolymer structure at atomic resolution from sequence alone remains a difficult problem, even for small sub-segments of large proteins. Such loop prediction challenges, which arise frequently in comparative…

A new convenient method for precise theoretical calculations of quantities of traditional theory of nuclear reactions such as widths of resonances (including sub-threshold), and asymptotic normalization coefficients is proposed. This method…

We propose a unique scheme to construct fully optimized atomic basis sets for density-functional calculations. The shapes of the radial functions are optimized by minimizing the {\it spillage} of the wave functions between the atomic…

Creating accurate, analytic atom--atom potentials for small organic molecules from first principles can be a time-consuming and computationally intensive task, particularly if we also require them to include explicit polarization terms,…

A variant of the Vanderbilt ultrasoft pseudopotential scheme, where the normconservation is released for only one or a few angular channels, is presented. Within this scheme some difficulties of the truly ultrasoft pseudopotentials are…

By employing the Coulomb proximity potential model (CPPM) in conjunction with 22 distinct proximity potential models, we investigated the temperature dependence and the effects of proton number and neutron number on the diffusion parameters…

Quantum computers hold immense potential in the field of chemistry, ushering new frontiers to solve complex many body problems that are beyond the reach of classical computers. However, noise in the current quantum hardware limits their…

Scientific foundation models hold great promise for advancing nuclear and particle physics by improving analysis precision and accelerating discovery. Yet, progress in this field is often limited by the lack of openly available large scale…

A low-energy hardware implementation of deep belief network (DBN) architecture is developed using near-zero energy barrier probabilistic spin logic devices (p-bits), which are modeled to realize an intrinsic sigmoidal activation function. A…

The $GW$ approximation has been recently gaining popularity among the method for simulating molecular core-level X-ray photoemission spectra. Traditionally, $GW$ core-level binding energies have been computed using either the cc-pV$n$Z or…