LLNL researchers have designed and produced, both conductive and non-conductive porous electrode components manufactured for improved metal deposition, discharging, and fluid dynamics in hybrid flow batteries.  This is achieved through Direct Ink Writing (DIW) additive manufacturing.  The engineered 3D electrodes enable uniform current distribution and even metal deposition during charging…

Improving the active material of the Zn anode is critical to improving the practicality of Zn-MnO2 battery technology. LLNL researchers have developed a new category of 3D structured Zn anode using a direct-ink writing (DIW) printing process to create innovative hierarchical architectures.  The DIW ink, which is a gel-based mixture composed of zinc metal powder and organic binders, is extruded…

Lawrence Livermore National Laboratory (LLNL) is offering the opportunity to collaborate in accelerating artificial intelligence (AI) for applied science, including research in key areas such as advanced material design, 3D printing, predictive biology, energy systems, “self-driving” lasers and fusion energy research.

Clinical images have a wealth of data that are currently untapped by physicians and machine learning (ML) methods alike. Most ML methods require more data than is available to sufficiently train them. In order to obtain all data contained in a clinical image, it is imperative to be able to utilize multimodal, or various types of, data such as tags or identifications, especially where spatial…

Some COVID-19 diagnoses are utilizing computed tomography (CT)-scans for triage. CT-scans produce immediate results with high sensitivity. The digital images produced by a CT-scan require physicians to identify objects within the image to determine the presence of disease. Object identification can be done using machine learning (ML) techniques such as deep learning (DL) to improve speed and…

MimicGAN represents a new generation of methods that can “self-correct” for unseen corruptions in the data out in the field. This is particularly useful for systems that need to be deployed autonomously without needing constant intervention such as Automated Driver Assistance Systems. MimicGAN achieves this by treating every test sample as “corrupt” by default. The goal is to determine (a) the…

LLNL has developed a new system, called the Segmentation Ensembles System, that provides a simple and general way to fuse high-level and low-level information and leads to a substantial increase in overall performance of digital image analysis. LLNL researchers have demonstrated the effectiveness of the approach on applications ranging from automatic threat detection for airport security, to…