Related papers: Electronic band structure screening for Dirac poin…
A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline…
We study the structural, electronic, and magnetic properties of Co$_2$ZAl compounds employing a pseudopotential electronic bandstructure method. The stability of the compounds is established through the formation and cohesive energy…
Machine learning (ML) plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules. However, most existing ML models for molecular electronic properties use density…
Systematic and automatic calculations of the electronic band structure are a crucial component of computationally-driven high-throughput materials screening. An algorithm, for any crystal, to derive a unique description of the crystal…
Knowing the band structure of materials is one of the prerequisites to understand their properties. Therefore, especially in the last decades, angle-resolved photoemission spectroscopy (ARPES) has become a highly demanded experimental tool…
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy…
Recent advances in machine learning (ML) have expedited materials discovery and design. One significant challenge faced in ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their…
Understanding material composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies focus on individual materials, we conducted a global mapping…
Density-functional studies of the electronic structures and exchange interaction parameters have been performed for a series of ferromagnetic full Heusler alloys of general formula Co$_2$MnZ (Z = Ga, Si, Ge, Sn), Rh$_2$MnZ (Z = Ge, Sn, Pb),…
The rapid development of computational materials science powered by machine learning (ML) is gradually leading to solutions to several previously intractable scientific problems. One of the most prominent is machine learning interatomic…
First-principles calculations using the plane-wave pseudopotential method within the generalized gradient approximation method were performed to study the pressure dependence of the structural, elastic, electronic and optical properties for…
In this work, we performed tight binding calculations of the electronic structure of III-V semiconductors compounds and their alloys based on the Extended H\"uckel Theory (EHT), where the H\"uckel parameters for the binary compounds were…
We present an online graphical pattern search tool for electronic band structure data contained within the Organic Materials Database (OMDB) available at https://omdb.diracmaterials.org/search/pattern. The tool is capable of finding…
The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open…
In recent years, machine learning (ML) has gained significant popularity in the field of chemical informatics and electronic structure theory. These techniques often require researchers to engineer abstract "features" that encode chemical…
Designing metallic glasses in silico is a major challenge in materials science given their disordered atomic structure and the vast compositional space to explore. Here, we tackle this challenge by finding optimal compositions for target…
Many-body physics of electron-electron correlations plays a central role in condensed mater physics, it governs a wide range of phenomena, stretching from superconductivity to magnetism, and is behind numerous technological applications. To…
Electronic transitions involving core-level orbitals offer a localized, atomic-site and element specific peek window into statistical systems such as molecular liquids. Although formally understood, the complex relation between structure…
There is an increasing reckoning that the thermoelectric performance of a material is dependent on its microstructure. However, the microstructure-properties relationship often remains elusive, in part due to the complexity of the hierarchy…
Based on high-throughput density functional theory calculations, we evaluate the local magnetic moments and M\"ossbauer properties for Fe-based intermetallic compounds and employ machine learning to map the local crystalline environments to…