Accelerating Materials Simulation with MAD3: A Machine Learning Breakthrough from Sandia National Laboratories
Date: Tuesday, January 20, 2026
Time: 2:00 PM ET / 11:00 AM PT
[Register Now]
Join us for a National Lab Discovery Series webinar featuring a powerful software innovation from Sandia National Laboratories that is changing how engineers simulate material behavior in design and manufacturing.
Materials Data Driven Design (MAD3) is a simulation platform that helps engineers and manufacturers predict how metals will deform in real-world applications. Unlike traditional methods that rely on costly testing or slow, compute-heavy simulations, MAD3 delivers fast and accurate results using machine learning and materials science principles.
This innovation provides critical support for designing components across aerospace, automotive, and manufacturing industries. It helps reduce product development time, avoid costly failures, and optimize performance—all with a user-friendly interface and no specialized hardware required.
What You’ll Learn:
- How MAD3 predicts directional mechanical behavior (plastic anisotropy) in metals with speed and accuracy
- How machine learning is applied to overcome common material simulation bottlenecks
- Where it fits in workflows for forming, stamping, and structural simulation
- How companies can evaluate, license, and co-develop the technology with Sandia
- Live Q&A with the Sandia team behind MAD3
Who Should Join:
- R&D directors and VPs of product development in manufacturing, aerospace, or automotive sectors
- Simulation and modeling leads evaluating new tools for internal integration
- Heads of advanced materials, manufacturing, process engineering, or innovation teams
- Business development professionals seeking technical partnerships
- Technology scouts and IP managers exploring licensing opportunities
- Corporate innovation officers interested in next-gen materials capabilities
About the Technology
MAD3 addresses a prevalent challenge in materials engineering: predicting how metals like aluminum or steel deform when shaped and formed. These materials are made of small internal crystals, referred to as grains, that determine directional mechanical properties. Understanding this behavior is critical for accurate simulations, yet it has historically posed significant modeling difficulties.
MAD3 integrates machine learning with domain expertise to address this challenge. The software accurately predicts key anisotropic mechanical parameters while cutting simulation time drastically. It uses an intuitive web-based graphical interface deployed using cloud technology, enabling it to run on any operating system without the need for specialized software or hardware.
Sandia is currently seeking industry partners to evaluate, license, and co-develop MAD3 for commercial deployment. Interested organizations are encouraged to connect with Sandia’s Licensing and Technology Transfer Office.
Learn how MAD3 is accelerating the future of materials engineering.