Related papers: Thermoelastic properties of bridgmanite using Deep…

Density functional theory (DFT) is probably the most promising approach for quantum chemistry calculations considering its good balance between calculations precision and speed. In recent years, several neural network-based functionals have…

We report \textit{ab initio} (LDA + U$_{sc}$) calculations of thermoelastic properties of ferric iron (Fe$^{3+}$)- and aluminum (Al)-bearing bridgmanite (MgSiO$_3$ perovskite), the main Earth forming phase, at relevant pressure and…

Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However, lower errors on held out test sets do not always translate…

Density functional theory (DFT) and thermal DFT (thDFT) calculations were used to evaluate the energy band structure, bandgap, and the total energy of various graphene quantum dots (GQDs). The DFT calculations were performed using local…

This work presents a machine learning approach to predict novel perovskite oxide materials in the Pr-Al-O and Pr-Sc-O compound families with the potential for photoluminescence applications. The predicted materials exhibit a large bandgap…

Computational studies of the thermodynamic properties of materials at the mesoscopic and macroscopic scales -- involving lengths and times of at least $\mu$m and $\mu$s, respectively -- rely on a coarse-graining approximation such that only…

Plutonium dioxide is of high technological importance in nuclear fuel cycle and is particularly crucial in long-term storage of Pu-based radioactive waste. Using first-principles density-functional theory, in this paper we systematically…

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet…

In this paper, we employ CASTEP based on DFT (density functional theory) calculations to investigate various physical properties of BaVO3, SrVO3, CaVO3 and PbVO3. The elastic constants, bulk modulus, Shear modulus, Young's modulus, Pugh's…

Simulation of reasonable timescales for any long physical process using molecular dynamics (MD) is a major challenge in computational physics. In this study, we have implemented an approach based on multi-fidelity physics informed neural…

We investigate the superconductivity of a three-dimensional d-p model with a multilayer perovskite structure on the basis of the second-order perturabation theory within the weak coupling framework. Our model has been designed with…

The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across different length scales. Unlike conventional data-driven…

Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical…

Metal halide perovskite (MHP) optoelectronics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors…

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability…

Halide perovskites are a promising class of materials for optoelectronic applications, due to their excellent optoelectronic performance. However, they suffer several dynamical degradation problems, the characterization of which is…

Reactive chemistry of molecular hydrogen at surfaces, notably dissociative sticking and hydrogen evolution, plays a crucial role in energy storage and fuel cells. Theoretical studies can help to decipher underlying mechanisms and reaction…

A versatile method for combining density functional theory (DFT) in the local density approximation (LDA) with dynamical mean-field theory (DMFT) is presented. Starting from a general basis-independent formulation, we use Wannier functions…

The discovery of very large piezo- and pyroelectric effects in ZrO2 and HfO2-based thin films opens up new opportunities to develop silicon-compatible sensor and actor devices. The effects are amplified close to the polar-orthorhombic to…

Machine learning (ML) models for predicting gas permeability through polymers have traditionally relied on experimental data. While these models exhibit robustness within familiar chemical domains, reliability wanes when applied to new…