Related papers: Wenzhou TE: a first-principles calculated thermoel…

Tellurium (Te) is an intrinsically p-type doped narrow bandgap semiconductor with excellent electrical conductivity and low thermal conductivity. Bulk trigonal Te has been theoretically predicted and experimentally demonstrated to be an…

Thermoelectrics (TE) materials manifest themselves in direct conversion of temperature differences to electric power and vice versa. Despite remarkable advances have been achieved in the past decades for various TE systems, the energy…

Incorporating Machine Learning (ML) into material property prediction has become a crucial step in accelerating materials discovery. A key challenge is the severe lack of training data, as many properties are too complicated to calculate…

Solid-state spin defects in wide-bandgap semiconductors are leading candidates for quantum information processing, but systematic identification of suitable host materials remains limited by the cost of first-principles screening across…

Most machine learning models for materials science rely on descriptors based on materials compositions and structures, even though the chemical bond has been proven to be a valuable concept for predicting materials properties. Over the…

The CALPHAD system of fundamental phase-level databases, now known as the Materials Genome, has enabled a mature technology of computational materials design and qualification that has already met the acceleration goals of the national…

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…

This work demonstrates how first-principles thermodynamic research within a Bayesian framework can quantify and propagate uncertainties to downstream thermodynamic calculations. To address the issue of Bayesian prior selection, knowledge of…

We use first-principles electronic structure methods to calculate the electronic thermoelectric properties (i.e. due to electronic transport only) of single-crystalline bulk $n$-type silicon-germanium alloys vs Ge composition, temperature,…

Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this article, we introduce…

Thermoelectric phenomena are traditionally associated with the interconversion of thermal and electrical energy in many-body systems. In the context of high-temperature quantum chromodynamics (QCD) matter produced in relativistic heavy-ion…

A dataset of 35,608 materials with their topological properties is constructed by combining the density functional theory (DFT) results of Materiae and the Topological Materials Database. Thanks to this, machine-learning approaches are…

A first-principles method, based on density functional perturbation theory, is presented for computing the leading order tunability of high-dielectric-constant materials.

We introduce a computational database with calculated structural, thermodynamic, electronic, magnetic, and optical properties of 820 one-dimensional materials. The materials are systematically selected and exfoliated from experimental…

As semiconductor technologies continue to scale down to the nanoscale, the efficient prediction of material properties becomes increasingly critical. The tight-binding (TB) method is a widely used semi-empirical approach that offers a…

Materials informatics (MI), emerging from the integration of materials science and data science, is expected to significantly accelerate material development and discovery. The data used in MI are derived from both computational and…

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational…

Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within…

The electronic structure and thermoelectric properties of SnSe are studied by first-principles methods. The inclusion of van der Waals dispersive corrections improves the agreement of structural parameters with experiments. The bands…

With the popularity of electric vehicles, the demand for lithium-ion batteries is increasing. Temperature significantly influences the performance and safety of batteries. Battery thermal management systems can effectively control the…