High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge.

Solid electrolytes hold substantial promise as vital components of all-solid-state batteries. Enhancing their performance necessitates simultaneous improvements in their stability and lithium conductivity. These properties can be calculated using first-principles simulations, provided that the crystal structure of the material and the diffusion pathway through the material are known.

Pericardial effusion after congenital heart surgery.

Objective: Pericardial effusion after cardiac surgery remains an important cause of morbidity and mortality. We describe the risk factors of pericardial effusion after congenital heart surgery through analyzing data from a nationwide, multi-institutional registry.

Methods: The Japan Congenital Cardiovascular Surgery Database, which reflects routine clinical care in Japan, was used for this retrospective cohort study.

Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.

Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery.

Audit-Based Quality Validation of the Japan Cardiovascular Surgery Database.

Background: The Japan Cardiovascular Surgery Database (JCVSD) is a nationwide registry of patients undergoing cardiovascular surgery in Japan. To investigate and improve data quality, we have been conducting on-site institutional audits since 2004. This study aimed to investigate the accuracy of the registered data by comparing it to site visit data.