The award provides funding to develop novel molecules and formulations for semiconductor manufacturing within four programmatic areas: PFAS-free process chemicals, catalysts, rare earth-free magnets, and battery systems. SandboxAQ will then advance the strongest breakthrough results into scaled domestic manufacturing and commercialization, via high-performing American manufacturing partners. This funding supports R&D across target categories in which foreign supply suppressed domestic production for decades and will ultimately strengthen national and economic security.

SandboxAQ will invest in enhancements to its ReAQT software platform and Large Quantitative Models (LQMs) to accelerate its work in virtually screening millions of candidate materials, after which it will select the most promising to validate with lab partners. LQMs are AI systems trained on the laws of physics, chemistry, and biology, not human language. What otherwise would take decades of laboratory trial-and-error can now run as a targeted, AI-driven campaign. The award allocates the funding across four material programmatic areas and for foundational investment in SandboxAQ's core LQM platforms for advanced chemical and materials development critical to semiconductor manufacturing. In connection with the award, the Department of Commerce will receive a minority, non-voting equity stake in SandboxAQ.

The four programmatic areas of the award are:

Per- and Polyfluoroalkyl substances (PFAS) are "forever chemicals" that appear throughout chip manufacturing as heat-transfer fluids, lubricants, insulating coatings, and surface treatment chemicals, and no compliant alternatives yet exist at scale. U.S. semiconductor factories that cannot certify PFAS-free alternatives may risk simultaneous supply disruption and regulatory exposure that could force production cutbacks in newly built domestic facilities. SandboxAQ has developed approaches to PFAS breakdown to address this issue and will build on this work with the CHIPS Act award.
Catalysts play critical roles throughout the semiconductor fabrication process, including in the generation of ultra-pure gases that enable materials to be precisely deposited one atomic layer at a time, and also to mitigate the resulting hazardous fluorinated exhaust that such processes produce. SandboxAQ will build on the progress already made by its AQCat workflows (which are built on 13.5 million high-fidelity quantum chemistry calculations developed in collaboration with NVIDIA) to screen catalyst candidates at near-quantum-chemistry accuracy 20,000 times faster than traditional methods, and reduce catalyst development timelines in commercial deployment. This recent paper details some of the catalyst work.
America's semiconductor factories depend on a primarily foreign-controlled supply of permanent magnets. China controls more than 90 percent of global neodymium-based permanent magnet production, and those magnets sit inside every advanced chip printing machine, vacuum pump, and precision actuator that positions silicon wafers to tolerances smaller than a virus. SandboxAQ will use ReAQT and its LQMs to screen magnet chemistries that eliminate or sharply reduce reliance on neodymium and other heavy rare earth elements, at a speed and precision no prior method has matched, targeting formulations that can be manufactured using existing U.S. production equipment lowering the capital barrier to commercialization.
The CHIPS Act was designed to rebuild domestic semiconductor manufacturing. Semiconductor fabrication factories require uninterrupted, precisely controlled, localized power. A current disturbance lasting only minutes can force tool shutdowns, reduce wafer yields, and trigger costly unplanned downtime. Most chip factory backup power systems depend on battery materials (lithium, cobalt, key chemical precursors) that are heavily concentrated overseas. As a result, a geopolitical or supply chain shock could potentially disrupt a U.S. semiconductor factory. SandboxAQ will build on the progress already made by its AQVolt workflows, which is a frontier AI model for battery chemistry. This programmatic area will develop battery chemistries that do not depend on lithium and other materials that have foreign chokepoints.