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Found 9 papers, 6 papers with code
Lessons in Reproducibility: Insights from NLP Studies in Materials Science
In summary, our study appreciates the benchmark set by these seminal papers while advocating for further enhancements in research reproducibility practices in the field of NLP for materials science.
Introducing flexible perovskites to the IoT world using photovoltaic-powered wireless tags
Our evaluation of the prototypes suggests that: i) flexible PV cells are durable up to a bending radius of 5 mm with only a 20 % drop in relative efficiency; ii) RFID communication range increased by 5x, and meets the energy needs (10-350 microwatt) to enable self-powered wireless sensors; iii) perovskite powered wireless sensors enable many battery-less sensing applications (e. g., perishable good monitoring, warehouse automation)
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up
While halide perovskites attract significant academic attention, examples of at-scale industrial production are still sparse.
Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems.
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties
Realizing general inverse design could greatly accelerate the discovery of new materials with user-defined properties.
Bridging the gap between photovoltaics R&D and manufacturing with data-driven optimization
Novel photovoltaics, such as perovskites and perovskite-inspired materials, have shown great promise due to high efficiency and potentially low manufacturing cost.
Applied Physics
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics
Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum.
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks
We overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data.
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks
X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.
