Related papers: Intrinsic structure of relaxor ferroelectrics from…
Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However,…
The molybdate oxides SrMoO$_3$, PbMoO$_3$, and LaMoO$_3$ are a class of metallic perovskites that exhibit interesting properties including high mobility, and unusual resistivity behavior. We use first-principles methods based on density…
Ferropericlase (Mg,Fe)O is a major lower mantle mineral, and studying its properties is a fundamental step toward understanding the Earth's interior. Here, we performed a first-principles investigation on the properties of iron as an…
Owing to the increasing popularity of lead-based hybrid perovskites for photovoltaic (PV) applications, it is crucial to understand their defect physics and its influence on their optoelectronic properties. In this work, we simulate various…
A linear regression-based machine learned interatomic potential (MLIP) was developed for the silicon-carbon system. The MLIP was predominantly trained on structures discovered through a genetic algorithm, encompassing the entire…
Unlike, momentum-dependent Rashba spin-splitting, materials exhibiting intrinsic momentum-independent unidirectional spin polarization also known as persistent spin texture (PST) in the full Brillouin zone are scarce. In this work, a list…
Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological…
The emergence of ferro- and ferrimagnetic behavior in insulating materials is uncommon, largely due to Hund's rules. Utilizing symmetry analysis, first-principles methods, and classical Monte Carlo simulations, \textcolor{black}{we report…
Macroscopic polarity and its dynamic response to external electric fields and temperature in the nominally ergodic relaxor phase of pristine lead magnesium niobate crystals and ceramics, Pb(Mg1/3Nb2/3)O3 (PMN), were investigated. Dynamic…
The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable…
We perform a thorough structural search with the minima hopping method (MHM) to explore low-energy structures of methylammonium lead iodide. By combining the MHM with a forcefield, we efficiently screen vast portions of the configurational…
Commencing from the centrosymmetric MnRMnSbO$_6$ compound, we explore the realm of magnetic polar double-double perovskite oxides characterized by significant ferroelectric polarization. Employing symmetry operations, first-principles…
Vibrational properties of solids are key to determining stability, response and functionality. However, they are challenging to computationally predict at Ab-Initio accuracy, even for elemental systems. Ab-Initio methods for modeling atomic…
Interest to ferroelectric materials has been increased significantly in last decades due to development of new generation of nonlinear optical and data storage devices. Scanning probe microscopy (SPM) can be used both for study of domain…
Machine learning potentials (MLPs) have significantly advanced global crystal structure prediction by enabling efficient and accurate property evaluations. In this study, global structure searches are performed for 11 bismuth-based binary…
The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental topic touching domains from planetary science to materials modeling. Yet, the nature of the LLT is still under debate. To resolve it,…
The research on multiferrocity and magnetoelectric coupling in metal-organic system is rare. Very few hybrid organic-inorganic frameworks (HOIF) exhibit direct magnetoelectric coupling (coupling between spins and dipoles) and also…
Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic…
Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic…
The computational prediction of the structure and stability of hybrid organic-inorganic interfaces provides important insights into the measurable properties of electronic thin film devices, coatings, and catalyst surfaces and plays an…