Related papers: Machine learning aided parameter analysis in Perov…
The halide perovskites have truly emerged as efficient optoelectronic materials and show the promise of exhibiting nontrivial topological phases. Since the bandgap is the deterministic factor for these quantum phases, here we present a…
High-purity germanium (HPGe) crystals underpin some of the most sensitive detectors used in fundamental physics and other high-resolution radiation-sensing applications. Despite their importance, the supply of detector-grade HPGe remains…
Achieving high-performance perovskite photovoltaics, especially in ambient air relies heavily on optimizing process parameters. However, traditional manual methods often struggle to effectively control the key variables. This inherent…
Understanding and controlling charge carrier recombination dynamics is essential for enhancing the performance of metal halide perovskite optoelectronic devices. In this study, we present a machine learning-assisted intensity-modulated…
A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications,…
The mechanical properties are essential for structural materials. The analyzed 360 data on four mechanical properties of steels, viz. fatigue strength, tensile strength, fracture strength, and hardness, are selected from the NIMS database,…
Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but…
The discovery and design of new materials which can efficiently catalyze the oxygen reduction and evolution reactions at reduced temperatures is important for facilitating the widespread adoption of fuel cell and electrolyzer technologies.…
Metal halide perovskite (MHP) optoelectronics may become a viable alternative to standard Si-based technologies, but the current lack of long-term stability precludes their commercial adoption. Exposure to standard operational stressors…
Over the last few years of the heyday of hybrid halide perovskites, so many metal cations additives have been tested to improve their optoelectronic properties that it is already difficult to find an element that has not yet been tried. In…
Property prediction accuracy has long been a key parameter of machine learning in materials informatics. Accordingly, advanced models showing state-of-the-art performance turn into highly parameterized black boxes missing interpretability.…
To advance the development of materials through data-driven scientific methods, appropriate methods for building machine learning (ML)-ready feature tables from measured and computed data must be established. In materials development, X-ray…
Quaternary III-V semiconductors are one of the major promising material classes in optoelectronics. The bandgap and its character, direct or indirect, are the most important fundamental properties determining the performance and…
We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic…
Machine learning techniques are utilized to estimate the electronic band gap energy and forecast the band gap category of materials based on experimentally quantifiable properties. The determination of band gap energy is critical for…
Machine-learning models are capable of capturing the structure-property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing…
We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are…
Due to their high photovoltaic efficiency and low-cost synthesis, lead halide perovskites have attracted wide interest for application in new solar cell technologies. The most stable and efficient ABX$_3$ perovskite solar cells employ mixed…
Metal-halide perovskites are promising materials for future optoelectronic applications. One intriguing property, important for many applications, is the tunability of the band gap via compositional engineering. While experimental reports…
Halide perovskites consist a class of materials under intense investigation due to their potential technological applications like solar cells, optoelectronic devices and catalysis. Recently we have studied using electronic band structure…