Quantum computing promises leaps in chemistry and materials science, and PNNL is preparing to realize its potential with a collaborative approach.

Christina Lomasney moderates panel on commercialization pathways for emerging artificial intelligence and high-performance computing technologies.

Brown University professor and PNNL joint appointee was honored for his exceptional work in applied mathematics.

The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

John VerWey, an advisor in the Mission Alignment group at PNNL, has recently been selected to lead a panel discussion at the inaugural Special Competitive Studies Project (SCSP) AI+ Compute & Connectivity Summit.

Formal mathematics both reveals what’s inside the black box of AI algorithms and pushes the boundary of what’s possible within math as a discipline.

Researchers can now quantify uncertainty in trained AI models aimed at solving difficult chemistry, biology and materials science problems.

CACTUS provides AI-powered cheminformatics for autonomous science.

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

A new generation of microelectronics research begins at PNNL.

Federal environmental permitting process to be fast-tracked with AI-powered tools and cloud-supported data analysis.

Samrat Chatterjee will serve on the editorial board of Risk Analysis, the flagship journal of the Society for Risk Analysis.

PNNL researchers work with industry partners to transform large language models.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

A team of researchers at PNNL is developing a new approach to explore the higher-dimensional shape of cyber systems to identify signatures of adversarial attacks.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

The speed and agility of cloud computing opens doors to completing advanced computational chemistry workflows in days instead of months.

Researchers discussed this topic at the 2024 Analog Computing for Science Workshop co-led by Antonino Tumeo.

ARQUIN computational pipeline provides a holistic framework for simulating a system with distributed quantum computing ingredients.