相关论文: Data-driven estimation of transfer integrals in un…

In this work, we use the artificial neural network (ANN) method to study and predict the distribution of strong coupling constants by fitting the existing data. Our approach takes advantage of the ability of ANN to learn complex nonlinear…

We present a novel pairing mechanism for superconductivity in strongly correlated electron systems, which often have both localised and itinerant charge carriers. An effective anisotropic interaction between the itinerant particles…

We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the…

The electron doping of undoped high-$T_c$ cuprates via the transfer of charge from manganites (or other oxides) using heterostructure geometries is here theoretically discussed. This possibility is mainly addressed via a detailed analysis…

Physics-Informed Neural Networks (PINNs), which integrate deep learning with physical prior knowledge, have proven to be a powerful tool for studying the dynamics of high-dimensional nonlinear systems. The present work utilizes PINNs to…

On the basis of the Hubbard model, we present the formulation of antiferromagnetism in electron-doped cuprates using the fluctuation-exchange approach. Taking into account the spin fluctuations in combination with the impurity scattering…

Accurate knowledge of the atomistic transition pathways in materials and material surfaces is crucial for many material science problems. However, conventional simulation techniques used to find these transitions are extremely…

Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To…

The inversion of DC resistivity data is a widely employed method for near-surface characterization. Recently, deep learning-based inversion techniques have garnered significant attention due to their capability to elucidate intricate…

In this paper we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully…

Synthetic medical image generation has evolved as a key technique for neural network training and validation. A core challenge, however, remains in the domain gap between simulations and real data. While deep learning-based domain transfer…

Herein, we present a new data-driven multiscale framework called FE${}^\text{ANN}$ which is based on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) as macroscopic surrogate models and an autonomous…

High critical temperature cuprate superconducting materials are composed of copper oxide layers and interlayer charge reservoirs. When not doped, these cuprates are antiferromagnetic insulators. We propose to design new materials by…

Using low-energy projection of the one-band t-t'-t"-Hubbard model we derive an effective spin-Hamiltonian and its spin-wave expansion to order 1/S. We fit the spin-wave dispersion of several parent compounds to the high-temperature…

Recently, a class of machine learning methods called physics-informed neural networks (PINNs) has been proposed and gained prevalence in solving various scientific computing problems. This approach enables the solution of partial…

Artificial Neural Networks (ANNs) have been successfully used in various nuclear engineering applications, such as predicting reactor physics parameters within reasonable time and with a high level of accuracy. Despite this success, they…

Nuclear quantum many-body methods rely on integral transform techniques to infer properties of electroweak response functions from ground-state expectation values. Retrieving the energy dependence of these responses is highly non-trivial,…

Spin-orbit corrections to superexchange are calculated using the method of Moriya [T.\ Moriya, {\it Phys.\ Rev.}~{\bf 120} 91, (1960)] for two of the insulating parent compounds of the cuprate superconductors: (1) La$_{2-x}$Nd$_x$CuO$_4$…

Inverse scattering problems are inherently challenging, given the fact they are ill-posed and nonlinear. This paper presents a powerful deep learning-based approach that relies on generative adversarial networks to accurately and…

Altermagnets offer a route to spin-polarized electronic states without macroscopic magnetization, because compensated magnetic order can generate momentum-dependent spin splitting through crystal-symmetry-controlled exchange fields.…