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BREADCRUMB

 1. Research
 2. Data Science & Computing
 3. Artificial Intelligence

ARTIFICIAL INTELLIGENCE

APPLYING MACHINE LEARNING
TO SCIENCE AND SECURITY CHALLENGES

Open

Pacific Northwest National Laboratory’s artificial intelligence and machine
learning methods and software packages are making a difference in operational
environments across the United States government and throughout the private
sector.


Section Menu
 * Scientific Discovery
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   * Artificial Intelligence
   * Graph and Data Analytics
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 * Lab Objectives
 * Publications & Reports
 * Featured Research

 * News & Updates
 * Attend an Artificial Intelligence Event
 * Publications
 * Projects
 * Explainer Articles



Over the past decade, artificial intelligence (AI) has experienced a
renaissance. AI enables machines to learn and make decisions without being
explicitly programmed.

AI has enabled a new generation of applications, opening the door to
breakthroughs in many aspects of daily life. From situational awareness to
threat analysis and detection, online signals to system assurance, Pacific
Northwest National Laboratory (PNNL) is advancing the frontiers of scientific
research and national security by applying AI to scientific problems.

The recently established Center for AI @PNNL drives strategic initiatives and
shapes the future direction of AI research at the laboratory. The Center is a
crucial component in supporting the Department of Energy’s Frontiers in
Artificial Intelligence for Science, Security and Technology (FASST) initiative,
which will drive advances in AI-ready data, frontier-scale compute, and safe,
secure, and trustworthy models, thereby accelerating discovery in science,
security, and technology.




STARTING WITH THE RIGHT ENVIRONMENT

For machine learning models, domain-specific knowledge can enhance
domain-agnostic data in terms of accuracy, interpretability, and defensibility.
Our AI research has been applied across a variety of domain areas from national
security to the electric grid and Earth systems. Leveraging a deep expertise in
the power grid domain, PNNL’s DeepGrid open-source platform uses deep
reinforcement learning to help power system operators create more robust
emergency control protocols—the safety net of our electric grid.

As part of the Physics-Informed Learning Machines for Multiscale and
Multiphysics Problems Center, our researchers are developing physics-informed
machine learning techniques. We are also developing generative models for
molecular structures and scalable implementations of deep reinforcement learning
algorithms. This work is part of the Exascale Computing Project’s codesign
center, ExaLearn.


BUILDING STRONGER AI SYSTEMS

PNNL takes a holistic approach to research focused on assuring the safety,
security, interpretability, explainability, and general robustness of AI-enabled
systems deployed in the real world. This research includes understanding and
mitigating system failures caused by design and development flaws, as well as
the malicious activities of adversaries.

Revealing the reasoning behind deep-learning-based decisions is a critical
component of assuring safety, security, and robustness. This reasoning allows
our researchers to assess complex systems from the perspective of digital and
physical system security, as well as from development and operational
perspectives.




FORECASTING REAL-WORLD EVENTS

PNNL’s research in content intelligence focuses on the development of novel AI
models to explain and predict social systems and behaviors related to national
security challenges in the human domain. Our expertise in descriptive,
predictive, and prescriptive analytics ranges from disinformation detection and
attribution to forecasting real-world events such as influenza
outbreaks and cryptocurrency prices.

PNNL’s interactive tools like CrossCheck, ESTEEM, and ErrFilter not only assure
we develop robust and generalizable AI models, but also advance understanding
and effective reasoning about extreme volumes of dynamic, multilingual, and
diverse real-world data.


INTEGRATING ACROSS MISSIONS

Data engineering is foundational to data science, focusing on information flow
from data sources to application. Combining this capability—including expertise
in data architectures and pipelines, data collection, and validation—with AI
enables cross-functional teams to provide optimal solutions to critical mission
spaces.

PNNL is teaming with Sandia National Laboratories and the Georgia Institute of
Technology on the Center for ARtificial Intelligence-focused ARchitectures and
Algorithms (ARIAA). The ARIAA team is exploring how to apply AI to address the
U.S. Department of Energy’s mission needs in cybersecurity, graph analytics, and
other areas.

We also partner with Oak Ridge National Laboratory, the University of Arizona,
and the University of California, Santa Barbara, to develop mathematical
foundations for data-driven decision control for complex systems, such as
high-energy physics facilities and smart buildings.

Further, our researchers integrate current approaches for scientific
high-performance computing, deep learning, and graph analytics computing
paradigms into a converged, coherent computing capability to accelerate
scientific discovery.




GAINING GREAT INSIGHTS FROM SMALL DATASETS

While almost all research on few-shot learning is done exclusively on images,
PNNL researchers have shown success in other data types, including text, audio,
and video. This has greatly expanded our AI capabilities beyond traditional,
publicly available image datasets and allows researchers to quickly build
machine learning models using small amounts of user-classified training examples

Sharkzor, for example, combines human interaction with machine learning
techniques to allow classification using just five to 10 images—far fewer than
the hundreds or thousands needed for traditional deep learning.

Learn more about how Sharkzor can be applied to nuclear forensics analysis. 


RECENT NEWS

OCTOBER 30, 2024
Feature


HUNTING FOR DARK MATTER AXIONS

Read

OCTOBER 28, 2024
Feature


COLLABORATION SPEEDS COMPLEX CHEMICAL MODELING

Read

OCTOBER 22, 2024
Staff Accomplishment


CHATTERJEE CO-AUTHORS WORKSHOP REPORT ON AGENTIC AI SYSTEM PREPAREDNESS

Read

SEE MORE RELATED NEWS


RESEARCH HIGHLIGHTS

OCTOBER 28, 2024
Article


ACCELERATING MATERIALS DISCOVERY WITH AI

Read

OCTOBER 22, 2024
Article


FRICTION STIR PROCESSING GETS A BOOST FROM NEW MACHINE LEARNING MODEL

Read

OCTOBER 10, 2024
Article


NEW FUNDING FOR FUSION MATERIALS PROJECTS

Read

SEE MORE RELATED HIGHLIGHTS


FACILITIES

Advanced Wireless Communications Lab Seattle Research Center


RELATED DIVISIONS

Advanced Computing, Mathematics, and Data
AI and Data Analytics Division


RELATED LINKS

PNNL Center for AIWhat is Deep Reinforcement Learning?What are Advanced
Computing Testbeds?What is Physics-informed Machine Learning?


CONTACTS

Court Corley
Chief Scientist for Artificial Intelligence
court@pnnl.gov
Mahantesh Halappanavar
Chief Data Scientist, Data Sciences & Machine Intelligence Group
hala@pnnl.gov
1 (509) 372-5987


LAB-LEVEL COMMUNICATIONS PRIORITY TOPICS

Computing


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