A team of researchers recently coordinated a series of international workshops aimed at enhancing chemical research security and fostering collaboration among scientists and academic researchers from both countries.
In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.
PNNL researchers helped design and conduct an international exercise hosted by the Ministry of Finance of Finland to help improve financial sector resilience.
A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.
A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.