A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

A new model accounts for co-occurring environmental parameters to improve predictions of a hurricane’s intensification.

Research indicates shifts in two climate oscillations drive spatial differences in Arctic atmospheric river trends over the past four decades.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

In this study, researchers used fire intensity data from satellites to map the distribution of aerosols into the atmosphere during a fire.

A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.

Scientists recently developed a flow direction model which produces high-quality flow routing parameters on any mesh system.

Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

Exploring why some brains are more resilient to the impact of Alzheimer’s disease could lead to improved diagnoses, treatment and prevention.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.