Related papers: Discovering Novel Halide Perovskite Alloys using M…
Accurate computational predictions of metal-organic frameworks (MOFs) and their properties is crucial for discovering optimal compositions and applying them in relevant technological areas. This work benchmarks density functional theory…
Crystal structures can be predicted from first-principles using ab initio random structure searching AIRSS and density functional theory (DFT). AIRSS provides a method to sample the potential energy landscape and DFT provides a robust and…
Optimal materials to induce bulk photovoltaic effects should lack inversion symmetry and have an optical gap matching the energies of visible radiation. Ferroelectric perovskite oxides such as BaTiO$_3$ and PbTiO$_3$ exhibit substantial…
Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid…
The perovskite oxides are known to be susceptible to structural distortions over a long wavelength when compared to their parent cubic structures. From an ab initio simulation perspective, this requires accurate calculations including many…
Designing materials with targeted properties remains challenging due to the vastness of chemical space and the scarcity of property-labeled data. While recent advances in generative models offer a promising way for inverse design, most…
Inorganic metal-halide cubic perovskite semiconductors have become more popular in industrial applications of photovoltaic and optoelectronic devices. Among various perovskites, lead-free materials are currently most explored due to their…
The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational…
Organic-inorganic metal halide perovskites (HaPs) are intensively studied for their light-harvesting properties. Owing to the interplay between strong electron-electron interaction and spin-orbit coupling (SOC), their quantitative…
Diffusion model deployment has been suffering from high energy consumption and inference latency despite its superior performance in visual generation tasks. Dynamic voltage and frequency scaling (DVFS) offers a promising solution to…
In the search for novel intermetallic ternary alloys, much of the effort goes into performing a large number of ab-initio calculations covering a wide range of compositions and structures. These are essential to build a reliable convex hull…
Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when…
Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…
Perovskite solar cells with record power conversion efficiency are fabricated by alloying both hybrid and fully inorganic compounds. While the basic electronic properties of the hybrid perovskites are now well understood, key electronic…
The accurate prediction of electronic and optical properties in chalcopyrite semiconductors has been a persistent challenge for density functional theory (DFT) based approaches. Addressing this issue, we demonstrate that very accurate…
Prediction of crystal system from X-ray diffraction (XRD) spectra is a critical task in materials science, particularly for perovskite materials which are known for their diverse applications in photovoltaics, optoelectronics, and…
Machine learning has emerged as a novel tool for the efficient prediction of materials properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional…
Compared to first principles studies like DFT (physically accurate, computationally expensive), the TB approach allows the disentanglement of the region of interest, to wit the fermi level. This allows the creation of simplified, highly…
The unprecedented structural flexibility and diversity of inorganic frameworks of layered hybrid halide perovskites (LHHPs) rise up a wide range of useful optoelectronic properties thus predetermining the extraordinary high interest to this…
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties. While several thousand 2D materials have been…