Advanced Computing, Mathematics, and Data

Current Projects

ARIAA

The co-design Center for Artificial Intelligence-focused Architectures and Algorithms (ARIAA) promotes the development of core technologies important for the application of artificial intelligence (AI) to Department of Energy (DOE) mission priorities.

AT SCALE

The Adaptive Tunability for Synthesis and Control via Autonomous Learning on Edge (AT SCALE) Initiative will transform materials synthesis through closed-loop autonomous experimentation. To achieve this goal, the AT SCALE team is reimagining the current synthesis-characterization-properties paradigm used in materials science.

CENATE

The Center for Advanced Technology Evaluation (CENATE) is a computing proving ground focused on integrated evaluation of early technologies to predict their potential and guide future systems design.

CHESS

The Cloud, High-Performance Computing (HPC), and Edge for Science and Security (CHESS) is a laboratory directed research and development effort which seeks to build capabilities to support integrated computing plans across three very different computing environments: cloud computing, high-performance computing, and edge computing.

SEA-CROGS

The Scalable, Efficient and Accelerated Causal Reasoning Operators, Graphs and Spikes for Earth and Embedded Systems (SEA-CROGS) Center advances scalable and efficient physics-informed machine intelligence to accelerate modeling, inference, causal reasoning, etiology, and pathway discovery for Earth systems, embedded systems, mobile platforms, and others by a thousand times.

TEC⁴

The Transferring Exascale Computational Chemistry to Cloud Computing Environment and Emerging Hardware Technologies (TEC⁴) project’s goal is to accelerate the transition from the basic research associated with developing and implementing electronic structure methods to their widespread use in solving complex challenges in industrial chemical sciences.

Data-Model Convergence

The Data-Model Convergence (DMC) Initiative is an ambitious, five-year effort to create the next generation of scientific computing capability through a multidisciplinary software and hardware co-design methodology.

Exascale Computing Project

The Exascale Computing Project (ECP) is responsible for developing the strategy, aligning the resources, and conducting the research and development necessary to achieve the nation’s imperative of delivering exascale computing by 2021. PNNL is a participating laboratory partner with the ECP.

PhILMs

PhILMs (Physics-Informed Learning Machines) investigators are encoding physics knowledge into machine learning to: 1) Design functional materials with tunable properties; 2) Solve longstanding problems exhibiting scaling cascades in combustion, subsurface and earth systems s; and 3) Establish probabilistic scientific computing as a new discipline at the interface of computational mathematics, multi-fidelity data, information fusion, and deep learning.