News

June 30, 2026

4 MIN READ

Supercharging silicon: ORNL scientists pinpoint new ways to synthesize power source

Researchers from ORNL and international collaborators discovered a simpler, lower-pressure method to produce R8 silicon, a rare form of silicon with promising applications in energy storage and electronics. By compressing amorphous (glassy) silicon instead of crystalline silicon, the team created R8 more efficiently, with neutron scattering, X-ray diffraction, and computer modeling confirming the process and suggesting it could also work for other materials like germanium.

December 12, 2024

11 MIN READ

Two paths, many benefits

ORNL researchers created and tested two methods for transforming coal into the scarce mineral graphite, which is used in batteries for electric vehicles.

September 26, 2022

6 MIN READ

Gang Seob “GS” Jung: Solving scientific puzzles using computational models

Gang Seob “GS” Jung has known from the time he was in middle school that he was interested in science.

July 18, 2022

3 MIN READ

Graph Convolutional Neural Networks for Fast and Accurate Predictions of Formation Energy and Mechanical Properties of Solid Solution Alloys with Multiple Crystal Structures

A graph convolutional neural network (GCNN) was trained to accurately predict formation energy and mechanical properties of solid solution alloys crystallized in different lattice structures, thereby advancing the design of alloys for improving mechanical performance.

January 11, 2022

2 MIN READ

Artificial Neural Network Potentials for Mechanics and Fracture Dynamics of Materials

March 25, 2021

1 MIN READ

Strain-Induced Growth of MoS2 Twisted Bilayers

Scientific Achievement: Strain developed during the coalescence of growing 2D crystals was shown to induce the nucleation of twisted bilayers with predictable twist angles. Significance and Impact: This work provides a pathway to synthetically control stacking angles in 2D heterostructures for continuously tunable optoelectronic and quantum properties.DOI: 10.1021/acsnano.0c08516