Physical Sciences and Mathematics Commons^™

Computational Screening Of New Perovskite Materials Using Transfer Learning And Deep Learning, Xiang Li, Yabo Dan, Rongzhi Dong, Zhuo Cao, Chengcheng Niu, Yuqi Song, Shaobo Li, Jianjun Hu

Faculty Publications

As one of the most studied materials, perovskites exhibit a wealth of superior properties that lead to diverse applications. Computational prediction of novel stable perovskite structures has big potential in the discovery of new materials for solar panels, superconductors, thermal electric, and catalytic materials, etc. By addressing one of the key obstacles of machine learning based materials discovery, the lack of sufficient training data, this paper proposes a transfer learning based approach that exploits the high accuracy of the machine learning model trained with physics-informed structural and elemental descriptors. This gradient boosting regressor model (the transfer learning model) allows us …

Semantic Segmentation Considering Image Degradation, Global Context, And Data Balancing, Dazhou Guo

Theses and Dissertations

Recently, semantic segmentation – assigning a categorical label to each pixel in an im- age – plays an important role in image understanding applications, e.g., autonomous driving, human-machine interaction and medical imaging. Semantic segmentation has made progress by using the deep convolutional neural networks, which are sur- passing the traditional methods by a large margin. Despite the success of the deep convolutional neural networks (CNNs), there remain three major challenges.

The first challenge is how to segment the degraded images semantically, i.e., de- graded image semantic segmentation. In general, image degradations increase the difficulty of semantic segmentation, usually leading to …

Convolutional Neural Networks For Crystal Material Property Prediction Using Hybrid Orbital-Field Matrix And Magpie Descriptors, Zhuo Cao, Yabo Dan, Zheng Xiong, Chengcheng Niu, Xiang Li, Songrong Qian, Jianjun Hu

Faculty Publications

Computational prediction of crystal materials properties can help to do large-scale in-silicon screening. Recent studies of material informatics have focused on expert design of multi-dimensional interpretable material descriptors/features. However, successes of deep learning such as Convolutional Neural Networks (CNN) in image recognition and speech recognition have demonstrated their automated feature extraction capability to effectively capture the characteristics of the data and achieve superior prediction performance. Here, we propose CNN-OFM-Magpie, a CNN model with OFM (Orbital-field Matrix) and Magpie descriptors to predict the formation energy of 4030 crystal material by exploiting the complementarity of two-dimensional OFM features and Magpie features. Experiments …