Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence.

Systematic evaluation of imaging techniques and baseline characteristics in patients with suspected vasculitis.

Purpose: To assess the diagnostic value of different imaging modalities in distinguishing systemic vasculitis from other internal and immunological diseases.

Methods: This retrospective study included 134 patients with suspected vasculitis who underwent ultrasound, magnetic resonance imaging (MRI), or F-fluorodeoxyglucose positron emission tomography/computed tomography (F-FDG PET/CT) between 01/2010 and 01/2019, finally consisting of 70 individuals with vasculitis. The main study parameter was the confirmation of the diagnosis using one of the three different imaging modalities, with the adjudicated clinical and histopathological diagnosis as the gold standard.

Comprehensive comparison of three different workstations for accurate planning of endovascular stent implantation in patients with thoracic aortic aneurysms.

Purpose: To assess the diagnostic precision of three different workstations for measuring thoracic aortic aneurysms (TAAs) in vivo and ex vivo using either pre-interventional computed tomography angiography scans (CTA) or a specifically designed phantom model.

Methods: This retrospective study included 23 patients with confirmed TAA on routinely performed CTAs. In addition to phantom tube diameters, one experienced blinded radiologist evaluated the dimensions of TAAs on three different workstations in two separate rounds.

Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy.

Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up calculations toward experimental guidance.

Machine Learning Approach to Analyze the Surface Properties of Biological Materials.

Similar to how CRISPR has revolutionized the field of molecular biology, machine learning may drastically boost research in the area of materials science. Machine learning is a fast-evolving method that allows for analyzing big data and unveiling correlations that otherwise would remain undiscovered. It may hold invaluable potential to engineer novel functional materials with desired properties, a field, which is currently limited by time-consuming trial and error approaches and our limited understanding of how different material properties depend on each other.