AI Accelerates Nuclear Forensics: A New Era in Analyzing Radioactive Materials

AI analysis of nuclear materials

The analysis of nuclear events—whether accidental, industrial, or explosive—has traditionally required slow and painstaking laboratory work. Identifying the origins and characteristics of radioactive materials in post-detonation debris is a monumental challenge, one that demands navigating through a storm of chemical reactions and isotopic transformations. But now, researchers at the Pacific Northwest National Laboratory (PNNL) are harnessing the power of AI and cloud computing to dramatically speed up the process.

Why Speed Matters in Nuclear Forensics

When a nuclear explosion or radiological event occurs, time is critical. Determining the source and composition of nuclear materials quickly is essential for global security, public health, and international response. However, analyzing the aftermath involves deconstructing a rapidly changing chemical landscape involving hundreds of isotopes and compounds.

Nic Uhnak, a radiochemist at PNNL, likens the task to reverse-engineering a baked cake to figure out its precise ingredients, the conditions in which it was baked, and the origins of each component. In the nuclear world, the stakes are far higher—and the complexity is orders of magnitude greater.

How AI and Cloud Computing Transform the Process

In their latest study, published in Physical Chemistry Chemical Physics, the PNNL team used generative AI, machine learning, and Microsoft Azure Quantum Elements to simulate the behavior of chemical species in post-detonation debris. Utilizing powerful NVIDIA H100 GPUs and over 55 terabytes of RAM, the researchers conducted extensive quantum chemistry simulations to calculate stability constants—values that determine how likely molecules are to bind or separate in solution.

These constants provide vital clues for guiding laboratory experiments and chemical separations. By prioritizing which reactions and separations to perform, the AI-driven model streamlines the analytical process, saving time and focusing resources on the most revealing data.

Real-World Applications and Future Implications

While this initial study is just a first step, it holds enormous promise. The same techniques could apply to other domains, such as the production and purification of medical isotopes like molybdenum-99, which is used for cancer diagnostics. Additionally, this research strengthens the nation’s nuclear forensics capabilities, helping government agencies respond more effectively to radiological events.

According to co-author Hadi Dinpajooh, "Generative AI allows us to explore far more molecular possibilities than traditional lab work ever could. It’s a new dimension in nuclear chemistry."

Collaboration Across Sectors

This work exemplifies the growing synergy between computational science, government research, and industry. With PNNL providing the nuclear expertise and Microsoft supplying cutting-edge cloud infrastructure, the project demonstrates how interdisciplinary collaboration can unlock new frontiers in science and national security.

Read the full article here: https://phys.org/news/2025-07-scientists-ai-analysis-nuclear-materials.html

Reference:

  • Mohammadhasan Dinpajooh et al., "On the stability constants of metal–nitrate complexes in aqueous solutions," Physical Chemistry Chemical Physics (2025). DOI: 10.1039/D4CP04295F

Sponsored by PWmat (Lonxun Quantum) – a leading developer of GPU-accelerated materials simulation software for cutting-edge quantum, energy, and semiconductor research. Learn more about our solutions at: https://www.pwmat.com/en

📘 Download our latest company brochure to explore our software features, capabilities, and success stories: PWmat PDF Brochure

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

#NuclearForensics #AI #ComputationalChemistry #Radiochemistry #NationalSecurity #QuantumServerNetworks #MaterialsScience #MachineLearning #AzureQuantum

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

CrystalGPT: Redefining Crystal Design with AI-Driven Predictions

Image
July 18, 2025 In a major leap for materials chemistry, researchers in the UK have developed Molecular Crystal Representation from Transformers (MCRT) —an AI model likened to ChatGPT for its transformative impact. Dubbed "CrystalGPT" , this foundation model rapidly predicts the physical properties of molecular crystals, revolutionizing how chemists design materials in silico . The AI Revolution in Crystal Design Understanding and predicting the properties of molecular crystals is critical for designing new materials for applications ranging from pharmaceuticals to energy storage. Traditional computational approaches, though powerful, are often resource-intensive and slow. Machine learning methods such as Smooth Overlap of Atomic Positions (SOAP) and atom-centred symmetry functions (ACSFs) have offered improvements, but they struggle with large datasets and subtle structural nuances. CrystalGPT, developed by a team led by Andy Cooper at the University of Liverpool, o...
Read more