Opportunities / ENERGY, DEPARTMENT OF
Simulator Collection for Atomic to Continuum Scales (SCACS)
| Notice type | Special Notice |
|---|---|
| Solicitation # | S-196281 |
| Department | ENERGY, DEPARTMENT OF |
| Sub-tier / office | ENERGY, DEPARTMENT OF / TRIAD - DOE CONTRACTOR |
| NAICS | 541715 — Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology) |
| PSC | AN15 |
| Set-aside | No set-aside |
| Posted | Aug 18, 2026 |
| Response deadline | Feb 16, 2027 (2027-02-15T17:00:00-07:00) |
| Place of performance | Los Alamos, NM, 87545 |
| Primary contact | Satya Srinivasan · licensing@lanl.gov |
Description (excerpt)
Engineers designing semiconductors, fusion reactors, spacecraft and advanced electronics need to know how heat and electricity will move through materials, at every microscopic location where a defect, grain boundary or interface could trigger failure. SCACS, developed by scientists at Los Alamos National Laboratory, delivers that insight by connecting two worlds that have long been disconnected: the atomic-scale physics that governs true material behavior and the continuum-scale engineering models used to design real devices. Built on novel atomic-site-projected conductivity methods and accelerated through graph neural networks, SCACS predicts spatially varying, direction-dependent thermal and electrical transport across large million-atom systems, giving material design Engineers a physics-grounded view of where hot-spots will form in materials, how defects will steer current and which microstructural choices will make or break performance. Overview The SCACS Toolkit is an AI-driven multiscale simulation platform designed to accelerate the development and deployment of advanced materials. Today, materials innovation is slowed by a fundamental gap: High-fidelity physics models (e.g., molecular dynamics) are too computationally expensive for real-world design, while the engineering-scale tools rely on simplified assumptions that limit predictive accuracy. This disconnect leads to costly trial-and-error development cycles and unexpected material failures in critical systems. SCACS bridges this gap by embedding machine-learned physics directly into engineering-scale simulations. Its core technology uses proprietary models Site-Projected Thermal Conductivity (SPTC-AI) and Site-Projected Electronic Conductivity (SPEC-AI) to translate first-principles insights into spatially resolved transport properties that can be used within standard finite element workflows. This approach enables accurate prediction of heat and electrical behavior in complex, heterogeneous materials at practical scales. The platform has broad commercial relevance across industries where thermal and electrical performance are critical, including semiconductors, energy systems, and advanced manufacturing. By reducing development time, improving reliability and lowering testing costs, SCACS offers a pathway to faster material qualification and more efficient product design, positioning it as a high-impact enabling technology for next-generation hardware innovation. Technology Description At it …