Related papers: Data-driven Discovery of 3D and 2D Thermoelectric …
We introduce a simple but efficient electronic fitness function (EFF) that describes the electronic aspect of the thermoelectric performance. This EFF finds materials that overcome the inverse relationship between $\sigma$ and $S$ based on…
The low energy band structure near the band gap determines the electrical performance of thermoelectric materials. Here, by using the hybrid-density functional theory (hybrid-DFT) calculations, we calculate the low energy band structure of…
We present a symmetry-based exhaustive approach to explore the structural and compositional richness of two-dimensional materials. We use a ``combinatorial engine'' that constructs potential compounds by occupying all possible Wyckoff…
We explore a material design strategy to optimize the thermoelectric power factor. The approach is based on screening the band structure changes upon a controlled volume change. The methodology is applied to the binary silicides and…
A thermoelectric device is a heat engine that directly converts heat into electricity. Many materials with a high figure of merit ZT have been discovered in anticipation of a high thermoelectric efficiency. However, there has been a lack of…
Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally…
To realize the creation of advanced multifunctional materials in energy storage and conversion technologies, the present research evaluates the structural, electronic, magnetic, thermodynamic, mechanical, thermoelectric, piezoelectric and…
The true power of computational research typically can lay in either what it accomplishes or what it enables others to accomplish. In this work, both avenues are simultaneously embraced across several distinct efforts existing at three…
In this work, density functional theory (DFT) is used to find out the ground state structures of A2AuScX6 (A= Cs, Rb; X= Cl, Br, I) double Perovskite (DP) halides for the first time. The DP A2AuScX6 halides were studied for their structural…
The combination of deep learning and ab initio materials calculations is emerging as a trending frontier of materials science research, with deep-learning density functional theory (DFT) electronic structure being particularly promising. In…
With the growth of computational resources, the scope of electronic structure simulations has increased greatly. Artificial intelligence and robust data analysis hold the promise to accelerate large-scale simulations and their analysis to…
An open-source data-analysis framework 2DMAT has been developed for experimental measurements of two-dimensional material structures. 2DMAT offers five analysis methods: (i) Nelder-Mead optimization, (ii) grid search, (iii) Bayesian…
In this study, we employ Graph Neural Networks (GNNs) to accelerate the discovery of novel 2D magnetic materials which have transformative potential in spintronics applications. Using data from the Materials Project database and the…
Computational screening has become a powerful complement to experimental efforts in the discovery of high-performance photovoltaic (PV) materials. Most workflows rely on density functional theory (DFT) to estimate electronic and optical…
We perform a computational screening for two-dimensional magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database. A recently proposed geometric descriptor…
Expanding the pool of stable halide perovskites with attractive optoelectronic properties is crucial to addressing current limitations in their performance as photovoltaic (PV) absorbers. In this article, we demonstrate how a…
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant…
The thermophysical properties of supported and free-standing nanomaterials would be different due to the substrate effect. To determine the thermophysical properties of supported 2D nanomaterials, this paper developed a two-step…
Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. Training machine…
Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under external time-dependent perturbations such as laser fields. In this work, we present a machine learning approach to accelerate…