Related papers: Machine Learning Based Prediction of Polaron-Vacan…
Polarized parton distributions and structure functions of the nucleon are analyzed in the improved valon model. The valon representation provides a model to represent hadrons in terms of quarks, providing a unified description of bound…
Interfacial polarization-charge accumulation at the heterointerface-is a well-established tool in semiconductors, but its influence in metals remains unexplored. Here, we demonstrate that interfacial polarization can robustly modulate…
The large-scale search for high-performing candidate 2D materials is limited to calculating a few simple descriptors, usually with first-principles density functional theory calculations. In this work, we alleviate this issue by extending…
This review will deal with several types of free charge localisation in oxides and their consequences on the effective dielectric spectra of such materials. The first one is the polaronic localisation at the unit cell scale on residual…
Rearrangement of atoms due to broken translational symmetries at the surface of SrTiO$_3$ is scarcely debatable in the present day scenario. Actual concern demands to unveil the true structure and precise mechanism responsible for atomic…
We perform full-potential screened-hybrid density-functional theory (DFT) calculations to compare the thermodynamic stability of neutral and charged states of the surface oxygen vacancy at the rutile TiO$_2$(110) surface. Solid-state…
The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the…
The magneto-optical properties of titanium dioxide systems are related to the presence of impurity states in the band gap due to oxygen vacancies. To understand about the interplay between localized electrons and structural distortions at…
A fundamental challenge in physics-informed machine learning (PIML) is the design of robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks require learning-to-learn from observations of the same (ODE)…
A Gaussian Approximation Potential (GAP) was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO2 facets through simulated annealing. Ab initio thermodynamics identifies (101) and…
Large scale first-principles calculations based on density functional theory (DFT) employing two different methods (atomic orbitals and plane wave basis sets) were used to study the energetics, geometry, the electronic charge redistribution…
The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs…
A model of precursor manifestation mechanisms, stimulated by tectonic activity and some peculiarities of observer strategy, whose main task is the effective measurement of precursors in the spatial area of their occurrence on the Earth's…
This paper shows how data-driven machine learning approaches can improve growth control, reproducibility, and physical insight in the pulsed laser deposition (PLD) growth of correlated oxides. Despite well-known relationships between growth…
Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural…
Neutron irradiation produces, within a few picoseconds, displacement cascades that are sequences of atomic collisions generating point and extended defects which subsequently affects the long-term evolution of materials. The diversity of…
Since their invention in the 1980s [1], optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics [2, 3, 4, 5]. Simulations of the motion of microscopic particles held by…
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here we show that a machine learning model,…
The diffusion-driven Turing instability is a potential mechanism for spatial pattern formation in numerous biological and chemical systems. However, engineering these patterns and demonstrating that they are produced by this mechanism is…