Accelerated materials language processing enabled by GPT

Materials language processing (MLP) is one of the key facilitators of materials science research, as it enables the extraction of structured information from massive materials science literature. Prior works suggested high-performance MLP models for text classification, named entity recognition (NER), and extractive question answering (QA), which require complex model architecture, exhaustive fine-tuning and a large number of human-labelled datasets. In this study, we develop generative pretrained transformer (GPT)-enabled pipelines where the complex architectures of prior MLP models are replaced with strategic designs of prompt engineering. First, we develop a GPT-enabled document classification method for screening relevant documents, achieving comparable accuracy and reliability compared to prior models, with only small dataset. Secondly, for NER task, we design an entity-centric prompts, and learning few-shot of them improved the performance on most of entities in three open datasets. Finally, we develop an GPT-enabled extractive QA model, which provides improved performance and shows the possibility of automatically correcting annotations. While our findings confirm the potential of GPT-enabled MLP models as well as their value in terms of reliability and practicability, our scientific methods and systematic approach are applicable to any materials science domain to accelerate the information extraction of scientific literature

Synergistic multi-doping effects on the Li7La3Zr2O12 solid electrolyte for fast lithium ion conduction.

Here, we investigate the doping effects on the lithium ion transport behavior in garnet Li7La3Zr2O12 (LLZO) from the combined experimental and theoretical approach. The concentration of Li ion vacancy generated by the inclusion of aliovalent dopants such as Al(3+) plays a key role in stabilizing the cubic LLZO. However, it is found that the site preference of Al in 24d position hinders the three dimensionally connected Li ion movement when heavily doped according to the structural refinement and the DFT calculations. In this report, we demonstrate that the multi-doping using additional Ta dopants into the Al-doped LLZO shifts the most energetically favorable sites of Al in the crystal structure from 24d to 96 h Li site, thereby providing more open space for Li ion transport. As a result of these synergistic effects, the multi-doped LLZO shows about three times higher ionic conductivity of 6.14 × 10(-4) S cm(-1) than that of the singly-doped LLZO with a much less efforts in stabilizing cubic phases in the synthetic condition

Human Pose Estimation in Extremely Low-Light Conditions

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camera system and build a new dataset of real low-light images with accurate pose labels. Thanks to our camera system, each low-light image in our dataset is coupled with an aligned well-lit image, which enables accurate pose labeling and is used as privileged information during training. We also propose a new model and a new training strategy that fully exploit the privileged information to learn representation insensitive to lighting conditions. Our method demonstrates outstanding performance on real extremely low light images, and extensive analyses validate that both of our model and dataset contribute to the success.Comment: Accepted to CVPR 202

Nb-doped TiO2 air-electrode for advanced Li-air batteries

As new substrate materials to replace a conventional carbon substrate, TiO2 and Nb-doped TiO2 air-electrodes for Li-air batteries were investigated. Through a simple two-step process, we successfully synthesized anatase Nb-doped TiO2 nanoparticles and demonstrated the potential applicability of TiO2-based materials for use in Li-air battery electrode. An air-electrode with Nb-doped TiO2 nanoparticles could deliver a higher discharge capacity than a bare TiO2 electrode due to the enhanced conductivity, which implies the importance of facile electron transport during the discharge process. © 2014 The Ceramic Society of Japan and the Korean Ceramic Society.

Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications

In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G.Comment: 17 pages, 6 figures, submitted to IEEE Network Magazin