Artificial Intelligence Projects | Argonne National Laboratory

Artificial intelligence for science will transform and accelerate discoveries.

Deep Learning to Analyze Galaxy-scale Lensing Systems

A team at Argonne is training a modular deep learning pipeline for denoising and deblending gravitational lensing systems.

Physical Sciences and Engineering

AI4PFAS: A Deep Transfer Learning Workflow to Examine the Toxicity of PFAS in the Environment

A computational modeling suite for toxicity estimation of PFAS “forever chemicals”

Computing, Environment and Life Sciences

Using Machine Learning Force Fields to Model Battery Structure

Argonne is developing a physics-informed machine learning approach to discover compatible cathodes, thereby overcoming barriers to commercialization of solid-state lithium-ion batteries.

Computing, Environment and Life Sciences

Enhanced Isotope Classification for Nuclear Reactions

Implementation of a machine-learning-based technique provides a new level of sensitivity for data collected in nuclear reactions.

Physical Sciences and Engineering

Calibrating Tomography Data Using Machine Learning

An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.

Photon Sciences

A Sea Slug Provides Inspiration to Advance AI

From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.

Photon Sciences

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