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This image illustrates lattice distortion, strain, and ion distribution in metal halide perovskites, which can be induced by external stimuli such as light and heat. Image credit: Stephen Jesse/ORNL

ORNL multimodal study sheds new light on promising photovoltaic material

A study by researchers at the ORNL takes a fresh look at what could become the first step toward a new generation of solar batteries.

QLAN submit - A team from the U.S. Department of Energy’s Oak Ridge National Laboratory, Stanford University and Purdue University developed and demonstrated a novel, fully functional quantum local area network, or QLAN, to enable real-time adjustments to information shared with geographically isolated systems at ORNL using entangled photons passing through optical fiber. Credit: Carlos Jones/ORNL, U.S. Dept. of Energy

QIS investments position ORNL to lead the quantum revolution

A rapidly emerging consensus in the scientific community predicts the future will be defined by humanity’s ability to exploit the laws of quantum mechanics.

The Energy Exascale Earth System Model project reliably simulates aspects of earth system variability and projects decadal changes that will critically impact the U.S. energy sector in the future. A new version of the model delivers twice the performance of its predecessor. Credit: E3SM, Dept. of Energy

Updated exascale system for Earth simulations delivers twice the speed

A new version of the Energy Exascale Earth System Model, or E3SM, is two times faster than an earlier version released in 2018.

This protein drives key processes for sulfide use in many microorganisms that produce methane, including Thermosipho melanesiensis. Researchers used supercomputing and deep learning tools to predict its structure, which has eluded experimental methods such as crystallography. Credit: Ada Sedova/ORNL, U.S. Dept. of Energy

Scientists use Summit supercomputer, deep learning to predict protein functions at genome scale

A team of scientists led by the Department of Energy’s Oak Ridge National Laboratory and the Georgia Institute of Technology is using supercomputing and revolutionary deep learning tools to predict the structures and roles of thousands of proteins with unknown functions.

Using quantum Monte Carlo methods, the researchers simulated bulk VO2. Yellow and turquoise represent changes in electron density between the excited and ground states of a compound composed of oxygen, in red, and vanadium, in blue, which allowed them to evaluate how an oxygen vacancy, in white, can alter the compound’s properties. Credit: Panchapakesan Ganesh/ORNL, U.S. Dept. of Energy

Artificially altered material could accelerate neuromorphic device development

Neuromorphic devices — which emulate the decision-making processes of the human brain — show great promise for solving pressing scientific problems, but building physical systems to realize this potential presents researchers with a significant

A material’s spins, depicted as red spheres, are probed by scattered neutrons. Applying an entanglement witness, such as the QFI calculation pictured, causes the neutrons to form a kind of quantum gauge. This gauge allows the researchers to distinguish between classical and quantum spin fluctuations. Credit: Nathan Armistead/ORNL, U.S. Dept. of Energy

Key witness helps scientists detect ‘spooky’ quantum entanglement in solid materials

A team led by the U.S. Department of Energy’s Oak Ridge National Laboratory demonstrated the viability of a “quantum entanglement witness” capable of proving the presence of entanglement between magnetic particles, or spins, in a quantum material.

Deeksha Rastogi uses high-performance computing to understand the human impacts of climate change. Credit: Carlos Jones, ORNL/U.S. Dept. of Energy

Deeksha Rastogi: Modeling climate extremes for community impact

An international problem like climate change needs solutions that cross boundaries, both on maps and among disciplines. Oak Ridge National Laboratory computational scientist Deeksha Rastogi embodies that approach.

As part of the Next-Generation Ecosystem Experiments Arctic project, scientists are gathering and incorporating new data about the Alaskan tundra into global models that predict the future of our planet. Credit: ORNL/U.S. Dept. of Energy

ORNL expertise supports latest IPCC report and efforts to understand, address climate change

Improved data, models and analyses from ORNL scientists and many other researchers in the latest global climate assessment report provide new levels of certainty about what the future holds for the planet 

Peter Thornton, right, works with Robertsville Middle School students to assemble the RamSat. Credit: Ian Goethert/ORNL, U.S. Dept. of Energy

Oak Ridge students, mentors count down to launch of wildfire-studying satellite to space station

RamSat’s mission is to take pictures of the forests around Gatlinburg, which were destroyed by wildfire in 2016. The mission is wholly designed and carried out by students, teachers and mentors, with support from numerous organizations, including Oak Ridge National Laboratory.

ORNL’s Sergei Kalinin and Rama Vasudevan (foreground) use scanning probe microscopy to study bulk ferroelectricity and surface electrochemistry -- and generate a lot of data. Credit: Jason Richards/ORNL, U.S. Dept. of Energy

ORNL’s superb materials expertise, data and AI tools propel progress

At the Department of Energy’s Oak Ridge National Laboratory, scientists use artificial intelligence, or AI, to accelerate the discovery and development of materials for energy and information technologies.