Projects in this area focus on developing accurate and efficient physics-based models, artificial intelligence/machine learning (AI/ML) algorithms and simulation software to advance the study of water and aqueous solutions.

Argonne’s water-related materials projects focus on developing and characterizing various materials such as membranes, sorbents and catalysts relevant to water monitoring, treatment, conveyance and reuse, and water-enabled energy production.

This focus area concerns the scaleup of materials manufacturing and processing, including data-enabled cyber-manufacturing.

Argonne’s Computational Science Division develops state-of-the-art software codes to simulate flows ranging from low subsonic to hypersonic on massively parallel computing platforms.

Printed electronic devices (PED) hold significant promise for being able to meet the demand for devices that are smaller, smarter, cheaper, more flexible, in-situ, and require less power.

In this area, Argonne is harnessing various quantum mechanical features to perform highly sensitive measurements. Potential applications include imaging brain function, searching for gravitational waves, and hunting for dark matter.

Argonne is building quantum computing and networking infrastructure that use quantum phenomena to enable groundbreaking applications. This work employs supercomputers to simulate the behavior of the underlying quantum materials, devices and algorithms.

Projects in this area focus on development and characterization of materials for next-generation quantum systems, such as quantum bits or ​“qubits,” which are fundamental objects in QIS and form the basis of quantum computing and networking architectures.

CPS combines theory and large-scale computational resources to address scientific challenges in chemical and material systems.

AI technologies applied by Argonne National Laboratory are enabling cutting-edge imaging modalities that will help solve some of the most pressing scientific challenges of our era.