A research collaboration led by the University of York's Department of Physics has created open-source software to assist in the creation of quantum materials which could in turn vastly increase the world's supercomputing power.

Throughout the world the increased use of data centers and cloud supercomputing are consuming growing amounts of energy - quantum materials could help tackle this problem, say the researchers.

Quantum materials - materials that exploit unconventional quantum effects arising from the collective behavior of electrons - could perform tasks previously thought impossible, such as harvesting energy from the complete solar spectrum or processing vast amounts of data with low heat dissipation.

The design of quantum materials capable of delivering intense computing power is guided by sophisticated supercomputer programs capable of predicting how materials behave when 'excited' with currents and light signals. {module INSIDE STORY}

Computational modeling has now taken a 'quantum leap' forward with the announcement of the Quantum KITE initiative, a suite of open-source computer codes developed by researchers in Brazil, the EU and the University of York. KITE is capable of simulating realistic materials with unprecedented numbers of atoms, making it ideally suited to create and optimize quantum materials for a variety of energy and computing applications.

Dr. Aires Ferreira, a Royal Society University Research Fellow and Associate Professor of Physics, who leads the research group at the University of York, said:

"Our approach uses a new class of quantum simulation algorithms to help predict and tailor materials' properties for a wide range of applications ranging from solar cells to low-power transistors.

"The first version of the free, open-source KITE code already demonstrates very encouraging capabilities in an electronic structure and device-level simulation of materials.

"KITE's capability to deal with multi-billions of atomic orbitals, which to our knowledge is unprecedented in any area of quantum science, has the potential to unlock new frontiers in condensed matter physics and computational modeling of materials."

One of the key aspects of KITE is its flexibility to simulate realistic materials, with different kinds of inhomogeneities and imperfections.

Dr. Tatiana Rappoport from the Federal University of Rio de Janeiro in Brazil said, "This open-source software is our commitment to helping to remove barriers to realistic quantum simulations and to promote an open science culture. Our code has several innovations, including a 'disorder cell' approach to simulate imperfections within periodic arrangements of atoms and an efficient scheme for dealing with RAM intensive calculations that can be useful to other scientific communities and industry."

Artificial intelligence has the potential to improve efficiencies and precision in sleep medicine, resulting in more patient-centered care and better outcomes, according to a new position statement from the American Academy of Sleep Medicine.

Published online as an accepted paper in the Journal of Clinical Sleep Medicine, the position statement was developed by the AASM's Artificial Intelligence in Sleep Medicine Committee. According to the statement, the electrophysiological data collected during polysomnography -- the most comprehensive type of sleep study -- is well-positioned for enhanced analysis through AI and machine-assisted learning.

"When we typically think of AI in sleep medicine, the obvious use case is for the scoring of sleep and associated events," said lead author and committee Chair Dr. Cathy Goldstein, associate professor of sleep medicine and neurology at the University of Michigan. "This would streamline the processes of sleep laboratories and free up sleep technologist time for direct patient care." {module INSIDE STORY}

Because of the vast amounts of data collected by sleep centers, AI and machine learning could advance sleep care, resulting in more accurate diagnoses, prediction of disease and treatment prognosis, characterization of disease subtypes, precision in sleep scoring, and optimization and personalization of sleep treatments. Goldstein noted that AI could be used to automate sleep scoring while identifying additional insights from sleep data.

"AI could allow us to derive more meaningful information from sleep studies, given that our current summary metrics, for example, the apnea-hypopnea index, aren't predictive of the health and quality of life outcomes that are important to patients," she said. "Additionally, AI might help us understand mechanisms underlying obstructive sleep apnea, so we can select the right treatment for the right patient at the right time, as opposed to one-size-fits-all or trial and error approaches."

Important considerations for the integration of AI into the sleep medicine practice include transparency and disclosure, testing on novel data, and laboratory integration. The statement recommends that manufacturers disclose the intended population and goal of any program used in the evaluation of patients; test programs intended for clinical use on independent data; and aid sleep centers in the evaluation of AI-based software performance.

"AI tools hold great promise for medicine in general, but there has also been a great deal of hype, exaggerated claims, and misinformation," explained Goldstein. "We want to interface with industry in a way that will foster safe and efficacious use of AI software to benefit our patients. These tools can only benefit patients if used with careful oversight."

The position statement, and a detailed companion paper on the implications of AI in sleep medicine, are available on the Journal of Clinical Sleep Medicine website.