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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. S

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