New materials simulation software from Los Alamos National Laboratory could help engineers design better electronics, semiconductors, aerospace and space technologies, biotechnology and energy systems. The Simulator Collection for Atomic-to-Continuum Scales (SCACS) toolkit predicts material properties by using artificial intelligence to connect atomic-level material behavior with device-scale performance.
In a paper chosen as an Editor's Suggestion for Physical Review Materials, the scientists described their innovation’s heat-flow capability, but their broader vision is a unified platform that also predicts electrical transport and mechanical behavior.
How it works:
- Materials can fail in harsh environments because of heat, corrosion, radiation or mechanical stress.
- The SCACS platform helps engineers understand those failures by first looking at what is happening at the atomic level, where damage and weak points begin, and then mapping that information up to the device scale, where real components operate.
- In this way, SCACS does not just explain why a material fails; it also helps predict how that failure will affect the performance of an entire part or system, so better materials and designs can be chosen before anything is built.
Why this matters: SCACS closes a longstanding gap between detailed atomic-scale physics and practical device-scale simulation. Instead of relying on oversimplified material properties in tools such as finite element models, SCACS uses advanced methods that preserve local features such as defects, interfaces and directional heat or current flow.
Funding:
- The Laboratory Directed Research and Development program at Los Alamos National Laboratory through the Director's Postdoctoral Fellowship Program and the Exploratory Research program.
- The UC/LANL Entrepreneur Postdoctoral Accelerator Fellowship.
LA-UR-26-25205
More STE Highlights Stories
STE Highlights Home
Physicists use D-Wave chip to study quantum effects
How quantum tunneling helps magnets choose a state
Cunningham named to ASME’s 2026 Mechanical Engineering Watch List
Los Alamos grad student uses research, humor and outreach to make nuclear science approachable
Bouabid earns Springer Thesis Prize for neutrino studies
Award recognizes research conducted in part at Los Alamos
It’s boiling over! Acoustic AI hears what cameras can’t see
A new method uses sound to predict unsafe conditions
First-ever measurements unlock hidden properties of neptunium oxide
New insights could improve prediction and management of long-lived radioactive materials
No longer a shot in the dark: Predicting metal failure under stress
The design and safety of defense systems could benefit from this model