Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

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This perspective addresses the topic of harnessing the tools of artificial intelligence (AI) for boosting innovation in functional materials design and engineering as well as discovering new materials for targeted applications in energy storage, biomedicin ...

Formamidinium-lead-iodide (FAPbI3) has established itself as the state of the art for high solar-energy conversion efficiency in perovskite-based solar cells. FAPbI3 has a rich phase diagram, and it has been noted that long-range correlation between organi ...

This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...

Infrared and Raman spectroscopies are ubiquitous techniques employed in many experimental laboratories, thanks to their fast and non-destructive nature able to capture materials' features as spectroscopic fingerprints. Nevertheless, these measurements freq ...

Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that this may result in sub-optimal convergence due to the challenges associated ...

Nonlinear photoluminescence (N-PL) is a broadband photon emission arising from a nonequilibrium heated electron distribution generated at the surface of metallic nanostructures by ultrafast pulsed laser illumination. N-PL is sensitive to surface morphology ...

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its ...

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

We perform an error analysis of a fully discretised Streamline Upwind Petrov Galerkin Dynamical Low Rank (SUPG-DLR) method for random time-dependent advection-dominated problems. The time integration scheme has a splitting-like nature, allowing for potenti ...

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...

The subject of the present work is discovery and in-depth characterization of a new class of functional materials. Tuning of the bond polarity and orbital occupation with a goal of establishing balance between localization and delocalization of electrons - ...

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity conditio ...

The electronic density of states (DOS) quantifies the distribution of the energy levels that can be occupied by electrons in a quasiparticle picture and is central to modern electronic structure theory. It also underpins the computation and interpretation ...

The electron self-interaction is a long-standing problem in density functional theory and is particularly critical in the description of polarons. Polarons are quasiparticles involving charge localization coupled with self-induced lattice distortions. Sinc ...

Water quality prediction in the spatially heterogeneous environment is challenging as the importance of water quality parameters (WQPs) and the performance of prediction models may vary across space. Thus, this study proposed spatially adaptive machine lea ...

We report on deep-to-near-UV transient absorption spectra of core-shell Au/SiO2 and Au/TiO2 nanoparticles (NPs) excited at the surface plasmon resonance of the Au core, and of UV-excited bare anatase TiO2 NPs. The bleaching of the first excitonic transitio ...

The Berry curvature dipole (BCD) is a key parameter that describes the geometric nature of energy bands in solids. It defines the dipole-like distribution of Berry curvature in the band structure and plays a key role in emergent nonlinear phenomena. The th ...

Purpose: To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-enco ...

Empowered by ever-increasing computational power and algorithmic developments, electronic-structure simulations continue to drive research and innovation in materials science. In this context, ab-initio calculations offer an unbiased platform for the under ...

Range-separated hybrid functionals for accurate prediction of band gaps of extended systems

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Artificial Intelligence for Advanced Functional Materials: Exploring Current and Future Directions

Nanoscale size affects in $α$-FAPbI$_3$ evinced large-scale ab initio simulations

Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"

Automated all-functionals infrared and Raman spectra

TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression

Memristive Control of Plasmon-Mediated Nonlinear Photoluminescence in Au Nanowires

Quantum-mechanical effects in photoluminescence from thin crystalline gold films

Exciton migration in two-dimensional materials

Error estimates for SUPG-stabilised Dynamical Low Rank Approximations

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Murunskite - a new class of functional material

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Machine-learning the electronic density of states: electronic properties without quantum mechanics

Self-Interaction and Polarons in Density Functional Theory

Spatially adaptive machine learning models for predicting water quality in Hong Kong

Giant two-photon absorption of anatase TiO2 in Au/TiO2 core-shell nanoparticles

Berry curvature dipole generation and helicity-to-spin conversion at symmetry-mismatched heterointerfaces

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI

Spectral and thermodynamic properties of interacting electrons with dynamical functionals