Related papers: Thermophysical properties of FLiBe using moment te…
Machine Learning Potentials (MLPs) can enable simulations of ab initio accuracy at orders of magnitude lower computational cost. However, their effectiveness hinges on the availability of considerable datasets to ensure robust…
Microstructural properties of thin-film absorber layers play a vital role in developing high-performance solar cells. Scanning probe microscopy is frequently used for measuring spatially inhomogeneous properties of thin-film solar cells.…
Results are reported for single crystal specimens of Hf$_2$Te$_2$P and compared to its structural analogue Zr$_2$Te$_2$P, which was recently proposed to be a potential reservoir for Dirac physics.[1] Both materials are produced using the…
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTP) are polynomial-like functions of…
Using density functional theory (DFT), the structural, mechanical, electronic, thermal, and optical properties of Hf2AB (A = Pb, Bi) borides were studied, considering the pressure effect up to 50 GPa. The lattice constants were found to be…
MgSiO_3-perovskite (MgPv) plays a crucial role in the Earth's lower mantle. This study combines deep-learning potential (DP) with density functional theory (DFT) to investigate the structural and elastic properties of MgPv under lower…
Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting…
FAPbI$_3$ is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3$ remains poorly understood, with open questions surrounding…
This study provides an in-depth analysis of time series forecasting methods to predict the time-dependent deformation trend (also known as creep) of salt rock under varying confining pressure conditions. Creep deformation assessment is…
Machine-learned potentials (MLPs) trained on ab initio data combine the computational efficiency of classical interatomic potentials with the accuracy and generality of the first-principles method used in the creation of the respective…
A perspective magneto-thermo-electric cooling device (MTECD) comprising a central magnetocaloric (MC) material (e.g., Gd) sandwiched by two thermoelectric (TE) materials (e.g., MnP) is proposed. The presence of the TE materials in the MTECD…
Melting is a high temperature process that requires extensive sampling of configuration space, thus making melting temperature prediction computationally very expensive and challenging. Over the past few years, I have built two methods to…
This data set descriptor introduces a structured, high-resolution dataset of transient thermal simulations for a vertical axis of a machine tool test rig. The data set includes temperature and heat flux values recorded at 29 probe locations…
The influence of the temperature and its fluctuations on the ion saturation current and the floating potential, which are typical quantities measured by Langmuir probes in the turbulent edge region of fusion plasmas, is analysed by global…
The closely related Green-Kubo and Helfand moment approaches are applied to obtain the thermal conductivity tensor of $\beta$-1,3,5,7-tetranitro-1,3,5,7-tetrazoctane ($\beta$-HMX) at $T=300$ K and $P=1$ atm from equilibrium molecular…
A general set of methods is presented for calculating chemical potentials in solid and liquid mixtures using {\em ab initio} techniques based on density functional theory (DFT). The methods are designed to give an {\em ab initio} approach…
Rare-earth monopnictides have attracted much attention due to their unusual electronic and topological properties for potential device applications. Here, we study rock-salt structured lanthanum monopnictides LaX (X = P, As) by density…
To understand the behaviour of rocks under changing load or temperature conditions, the determination of physical parameters like pore pressure or temperature within the pore space is essential. Within this study, the implementation of a…
The allegedly unconventional superconducting phase of magic-angle twisted bilayer graphene (MATBG)1 has been predicted to possess extraordinary thermal properties, as it is formed from a highly diluted electron ensemble with both a…
Machine learning thermodynamic perturbation theory (MLPT) is a promising approach to compute finite temperature properties when the goal is to compare several different levels of ab initio theory and/or to apply highly expensive…