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Autonomous synthesis and characterization of inorganic materials requires the automatic and accurate analysis of X-ray diffraction spectra. For this task, we designed a probabilistic deep learning algorithm to identify complex multi-phase…
Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost,…
The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore…
Chemical bonding governs how atoms interact to form compounds, thereby determining their physicochemical properties. Despite being an elusive concept, chemical bonding has led to the development of models and tools to explain and predict…
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…
Multi-principal element alloys open large composition spaces for alloy development. The large compositional space necessitates rapid synthesis and characterization to identify promising materials, as well as predictive strategies for alloy…
Lead halide perovskites are a remarkable class of materials that have emerged over the past decade as being suitable for application in a broad range of devices, such as solar cells, light-emitting diodes, lasers, transistors, and memory…
The spectacular performance of halide perovskites in optoelectronic devices is rooted in their tolerance to defects. Previous studies showed that defects in these materials generate shallow electronic states. However, how these shallow…
Accurate grain orientation mapping is essential for understanding and optimizing the performance of polycrystalline materials, particularly in energy-related applications. Lithium nickel oxide (LiNiO$_{2}$) is a promising cathode material…
Chalcogenide perovskites have emerged as promising lead free materials for photovoltaic and thermoelectric applications. Among them, BaZrS3 has attracted particular attention due to its thermal and chemical stability, favorable…
Density functional theory (DFT) calculations are performed to predict the structural, electronic and magnetic properties of electrically neutral or charged few-atomic-layer (AL) oxides whose parent systems are based on polar perovskite…
With the rapid development of artificial intelligence, the combination of material database and machine learning has driven the progress of material informatics. Because aluminum alloy is widely used in many fields, so it is significant to…
We report the synthesis and characterization of a novel class of ionic semiconductor materials- inorganic chalcogenide perovskites. Several different compounds including BaZrS3, CaZrS3, SrTiS3 and SrZrS3 were synthesized by high temperature…
Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes and structures. In the current study,…
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…
An exciting development over the past few decades has been the use of high-throughput computational screening as a means of identifying promising candidate materials for a variety of structural or functional properties. Experimentally, it…
In this study, a machine learning-based technique is developed to reduce the computational cost required to explore large design spaces of substitutional alloys. The first advancement is based on a neural network approach to predict the…
We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts…
Selection of solution concentrations and flow rates for the fabrication of microfibers using a microfluidic device is a largely empirical endeavor of trial-and-error, largely due to the difficulty of modeling such a multiphysics process.…
High-temperature alloy design requires a concurrent consideration of multiple mechanisms at different length scales. We propose a workflow that couples highly relevant physics into machine learning (ML) to predict properties of complex…