SandboxAQ will use physics-based AI to develop, validate, and then commercialize critical formulations for American semiconductor manufacturing and to drive national security needs
PALO ALTO, Calif., June 17, 2026 /PRNewswire/ — SandboxAQ announced today a definitive agreement with the U.S. Department of Commerce’s CHIPS Research and Development Office for a $500 million award to address one of the most urgent challenges in American manufacturing: the foreign control of critical materials and chemistries that are essential to semiconductor manufacturing.
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.
“President Trump is committed to strengthening America’s semiconductor supply chain and ensuring national security,” said Secretary of Commerce Howard Lutnick. “This award will accelerate the discovery and innovation of critical materials and reduce our reliance on foreign-controlled materials.”
Jack Hidary, CEO of SandboxAQ, said: “Securing America’s semiconductor future means controlling the materials that drive this vital sector. SandboxAQ’s Large Quantitative Models are grounded in the engineering and physics needed to address the needs of our domestic semiconductor sector. This award from the U.S. Department of Commerce enables SandboxAQ to run advanced AI-driven programs across four critical material categories and then work with partners to scale the resulting formulations.”
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.
ReAQT : One Platform Across All Four Programmatic Areas
ReAQT, SandboxAQ’s AI simulation platform, is the foundation for all four material programmatic areas and is built to operate at scale. SandboxAQ plans to deepen its investment in ReAQT in order to accelerate the development timeline for new materials discovery. ReAQT generates its own physics-grounded training data through high-fidelity simulations, including Density Functional Theory, Molecular Dynamics, and reaction modeling, then trains SandboxAQ’s proprietary Large Quantitative Models (LQMs) on that data and integrates them directly into Design-Make-Test workflows. Because LQMs learn from physical laws and real-world data, they deliver accurate predictions about materials that have not been previously synthesized, giving researchers a reliable map of what is possible before committing to expensive lab work.
Dr. Stefan Leichenauer, Vice President of Engineering at SandboxAQ, said: “We built ReAQT around an insight that translates directly into competitive advantage. The most accurate simulation methods are too slow to search the range of materials that matter at scale. Models trained purely on existing data are fast but break down when applied to materials they have never seen. ReAQT solves both problems by generating its own high-quality training data grounded in physics, then training our Large Quantitative Models on it. The result is a platform that makes reliable predictions about materials, compressing development timelines in ways that shift what is commercially viable.”
Background: Key Research Papers and Published Resources
SandboxAQ published its frontier AI catalyst model in Nature NPJ Computational Materials and has a growing body of published technical and scientific results which can be found in its online library of papers.
Allam, O., Wander, B., Kim, S. et al. “AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis“, Nature NPJ Computational Materials (27 April, 2026).
Rask, A., Huntington, L., Kim, S. et al., “Breaking down per- and polyfluoroalkyl substances (PFAS): tackling multitudes of correlated electrons“, Chemical Science, 2025, Volume 16, 19099.
For further published work, see the SandboxAQ Technical Library: SandboxAQ Technical Research.
Transaction Advisors
Skadden, Arps, Slate, Meagher & Flom LLP, Wilson Sonsini, and Crowell & Moring LLP, and PwC acted as advisors to SandboxAQ.
About SandboxAQ
SandboxAQ is a B2B company delivering solutions at the intersection of AI and quantum techniques. The company’s Large Quantitative Models (LQMs) deliver critical advances in life sciences, financial services, navigation, and other sectors. SandboxAQ is an independent, growth-backed company funded by leading investors and strategic partners including funds and accounts advised by T. Rowe Price Associates, Inc., Google, Alger, IQT, US Innovative Technology Fund, S32, Paladin Capital, Eric Schmidt, Breyer Capital, Ray Dalio, Marc Benioff, Thomas Tull, and others. For more information, visit www.sandboxaq.com.
Cautionary Note Regarding Forward-Looking Statements
This release contains forward-looking statements. These statements are based on current expectations and assumptions that are subject to risks and uncertainties. We undertake no obligation to update or revise any forward-looking statement, whether as a result of new information, future events, or otherwise.
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