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Technical illustration of an EV battery collector with vehicle placement shown in background.

Strengthening the competitiveness of the U.S. transportation industry depends on developing domestic EV batteries that combine rapid charging with long-range performance — two goals that often conflict. Researchers at ORNL have addressed this challenge by redesigning a key battery component, enabling fast, 10-minute charging while improving energy density and reducing reliance on copper.

Illustration of melting point of lithium chloride, which is shown with green and blue structures in two rows.

Scientists have developed a new machine learning approach that accurately predicted critical and difficult-to-compute properties of molten salts, materials with diverse nuclear energy applications. 

ORNL researcher Priya Ranjan standing outside in front of brick pillars

From decoding plant genomes to modeling microbial behavior, computational biologist Priya Ranjan builds computational tools that turn extensive biological datasets into real-world insights. These tools transform the way scientists ask and answer complex biological questions that advance biotechnology breakthroughs and support cultivation of better crops for energy and food security. 

Three profile photos of ORNL researchers are cut out into one image representing three new members of leadership for the Center for Bioenergy Innovation

The Center for Bioenergy Innovation, or CBI, at the Department of Energy’s Oak Ridge National Laboratory has promoted Melissa Cregger and Carrie Eckert to serve as chief science officers, advancing the center’s mission of innovations for new domestic biofuels, chemicals and materials.

Large group photo outside on stairs at the Quantum Science Center all hands meeting.

Members of the Quantum Science Center, or QSC, gathered at an all-hands meeting in Baton Rouge, Louisiana, in mid-May to reflect on the remarkable accomplishments from the past five years and to prepare for what members hope to be the next five years of the center.

Paul Kairys

Paul is exploring the next frontier: bridging quantum computing with neutron science. His research aims to integrate quantum algorithms with neutron scattering experiments, opening new possibilities for understanding materials at an atomic level.

ORNL's Quantum Science Center Director is speaking to a attendee at Purdue University Quantum Science Center Summer School poster presentation

The fifth annual Quantum Science Center, or QSC, Summer School at Purdue University, held Apr. 21 through Apr. 25, 2025, welcomed its largest group of students to date. Experts from industry, academia and national laboratories gathered at the Purdue Quantum Science and Engineering Institute to share their research in multiple areas of quantum science.

Illustration of a real-time simulation showing a metallic nanoparticle’s optical response to light using RT-TDDFT. The image depicts electron oscillations and surrounding electromagnetic fields. Four inset panels represent applications: plasmon-enhanced biosensing, quantum computing, photochemical catalysis, and cancer detection through photothermal therapy.

A research team from the Department of Energy’s Oak Ridge National Laboratory, in collaboration with North Carolina State University, has developed a simulation capable of predicting how tens of thousands of electrons move in materials in real time, or natural time rather than compute time.

ORNL researcher Jesse Labbe is working with plants in a greenhouse. He is framed on all sides with bright green leaves

Jesse Labbé aims to leverage biology, computation and engineering to address societal challenges related to energy, national security and health, while enhancing U.S. competitiveness. Labbé emphasizes the importance of translating groundbreaking research into practical applications that have real-world impact.

Illustration of a glowing black box emitting digital particles that form into a 3D model of an electrical grid infrastructure, set against a background of binary code and data visualizations.

Researchers at Oak Ridge National Laboratory have developed a modeling method that uses machine learning to accurately simulate electric grid behavior while protecting proprietary equipment details. The approach overcomes a key barrier to accurate grid modeling, helping utilities plan for future demand and prevent blackouts.