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Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor‐intensive and time‐consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data‐driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials’ uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p‐type TE materials and 8 promising n‐type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.

 

This systematic study investigates the optical properties and process−structure−property relationships of Mn‐doped zinc oxide (ZnMnO) grown by metal‐organic chemical vapor deposition with varying Mn‐doping concentration and growth conditions. ZnMnO exhibits a good crystal quality oriented in the (002) direction and contains intermixtures of zinc oxide (ZnO)‐like and manganese oxide (Mn_xO_y)‐like phases. The material exhibits a direct energy absorption band‐edge and a reduction in bandgap with Mn‐doping. Photoluminescence studies show that Mn‐doping can simultaneously tailor broad green band luminescence and ultraviolet edge emissions. Post‐growth air‐annealing results in broad Mn_xO_y‐related photoluminescence emissions at 3.3–4.5 eV. A further reduction in the absorption band‐edge is also observed with annealing. Results indicate that luminescence wavelengths and intensities, and absorption band‐edge can be tuned with the Mn‐doping process. This paper promotes a thorough understanding of defect centers in ZnO with transition metal doping and their interrelation with optical characteristics. The work provides a solid foundation for the development of optoelectronic devices, such as light emitting diodes, solar cells, lasers, and photodetectors using ZnO‐based materials.

 

ABSTRACT The FaceBase Consortium was established by the National Institute of Dental and Craniofacial Research in 2009 as a ‘big data’ resource for the craniofacial research community. Over the past decade, researchers have deposited hundreds of annotated and curated datasets on both normal and disordered craniofacial development in FaceBase, all freely available to the research community on the FaceBase Hub website. The Hub has developed numerous visualization and analysis tools designed to promote integration of multidisciplinary data while remaining dedicated to the FAIR principles of data management (findability, accessibility, interoperability and reusability) and providing a faceted search infrastructure for locating desired data efficiently. Summaries of the datasets generated by the FaceBase projects from 2014 to 2019 are provided here. FaceBase 3 now welcomes contributions of data on craniofacial and dental development in humans, model organisms and cell lines. Collectively, the FaceBase Consortium, along with other NIH-supported data resources, provide a continuously growing, dynamic and current resource for the scientific community while improving data reproducibility and fulfilling data sharing requirements.