SandboxAQ Signs $500 Million Deal With Dept. of Commerce

<p><strong><span class&equals;"legendSpanClass">PALO ALTO <&sol;span><&sol;strong>&&num;8212&semi; <a href&equals;"https&colon;&sol;&sol;edge&period;prnewswire&period;com&sol;c&sol;link&sol;&quest;t&equals;0&amp&semi;l&equals;en&amp&semi;o&equals;4713273-1&amp&semi;h&equals;144983775&amp&semi;u&equals;http&percnt;3A&percnt;2F&percnt;2Fwww&period;sandboxaq&period;com&percnt;2F&amp&semi;a&equals;SandboxAQ" target&equals;"&lowbar;blank" rel&equals;"nofollow noopener">SandboxAQ<&sol;a> has signed an agreement with the U&period;S&period; Department of Commerce&&num;8217&semi;s CHIPS Research and Development Office for a &dollar;500 million award to address one of the most urgent challenges in American manufacturing&colon; the foreign control of critical materials and chemistries that are essential to semiconductor manufacturing&period;<&sol;p>&NewLine;<p>The award provides funding to develop novel molecules and formulations for semiconductor manufacturing within four programmatic areas&colon; PFAS-free process chemicals&comma; catalysts&comma; rare earth-free magnets&comma; and battery systems&period; SandboxAQ will then advance the strongest breakthrough results into scaled domestic manufacturing and commercialization&comma; via high-performing American manufacturing partners&period; This funding supports R&amp&semi;D across target categories in which foreign supply suppressed domestic production for decades and will ultimately strengthen national and economic security&period;<&sol;p>&NewLine;<p>SandboxAQ will invest in enhancements to its ReAQT software platform and Large Quantitative Models &lpar;LQMs&rpar; to accelerate its work in virtually screening millions of candidate materials&comma; after which it will select the most promising to validate with lab partners&period; LQMs are AI systems trained on the laws of physics&comma; chemistry&comma; and biology&comma; not human language&period; What otherwise would take decades of laboratory trial-and-error can now run as a targeted&comma; AI-driven campaign&period; The award allocates the funding across four material programmatic areas and for foundational investment in SandboxAQ&&num;8217&semi;s core LQM platforms for advanced chemical and materials development critical to semiconductor manufacturing&period; In connection with the award&comma; the Department of Commerce will receive a minority&comma; non-voting equity stake in SandboxAQ&period;<&sol;p>&NewLine;<p>SandboxAQ is a venture-backed company with investors including&colon; T&period; Rowe Price Associates&comma; Google&comma; Alger&comma; IQT&comma; US Innovative Technology Fund&comma; S32&comma; Paladin Capital&comma; Eric Schmidt&comma; Breyer Capital&comma; Ray Dalio&comma; Marc Benioff&comma; Thomas Tull&comma; and others&period;<&sol;p>&NewLine;<p>Jack Hidary&comma; CEO of SandboxAQ&comma; said&colon; &&num;8220&semi;Securing America&&num;8217&semi;s semiconductor future means controlling the materials that drive this vital sector&period; SandboxAQ&&num;8217&semi;s Large Quantitative Models are grounded in the engineering and physics needed to address the needs of our domestic semiconductor sector&period; This award from the U&period;S&period; 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&period;&&num;8221&semi;<&sol;p>&NewLine;<p>The four programmatic areas of the award are&colon;<&sol;p>&NewLine;<ol type&equals;"1">&NewLine;<li>Per- and Polyfluoroalkyl substances &lpar;PFAS&rpar; are &&num;8220&semi;forever chemicals&&num;8221&semi; that appear throughout chip manufacturing as heat-transfer fluids&comma; lubricants&comma; insulating coatings&comma; and surface treatment chemicals&comma; and no compliant alternatives yet exist at scale&period; U&period;S&period; 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&period; SandboxAQ has developed approaches to PFAS breakdown to address this issue and will build on this work with the CHIPS Act award&period;<&sol;li>&NewLine;<li>Catalysts play critical roles throughout the semiconductor fabrication process&comma; including in the generation of ultra-pure gases that enable materials to be precisely deposited one atomic layer at a time&comma; and also to mitigate the resulting hazardous fluorinated exhaust that such processes produce&period; SandboxAQ will build on the progress already made by its AQCat workflows &lpar;which are built on 13&period;5 million high-fidelity quantum chemistry calculations developed in collaboration with NVIDIA&rpar; to screen catalyst candidates at near-quantum-chemistry accuracy 20&comma;000 times faster than traditional methods&comma; and reduce catalyst development timelines in commercial deployment&period; <a href&equals;"https&colon;&sol;&sol;edge&period;prnewswire&period;com&sol;c&sol;link&sol;&quest;t&equals;0&amp&semi;l&equals;en&amp&semi;o&equals;4713273-1&amp&semi;h&equals;2082919055&amp&semi;u&equals;https&percnt;3A&percnt;2F&percnt;2Fwww&period;nature&period;com&percnt;2Farticles&percnt;2Fs41524-026-02099-6&amp&semi;a&equals;This&plus;recent&plus;paper" target&equals;"&lowbar;blank" rel&equals;"nofollow noopener">This recent paper<&sol;a> details some of the catalyst work&period;<&sol;li>&NewLine;<li>America&&num;8217&semi;s semiconductor factories depend on a primarily foreign-controlled supply of permanent magnets&period; China controls more than 90 percent of global neodymium-based permanent magnet production&comma; and those magnets sit inside every advanced chip printing machine&comma; vacuum pump&comma; and precision actuator that positions silicon wafers to tolerances smaller than a virus&period; 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&comma; at a speed and precision no prior method has matched&comma; targeting formulations that can be manufactured using existing U&period;S&period; production equipment lowering the capital barrier to commercialization&period;<&sol;li>&NewLine;<li>The CHIPS Act was designed to rebuild domestic semiconductor manufacturing&period; Semiconductor fabrication factories require uninterrupted&comma; precisely controlled&comma; localized power&period; A current disturbance lasting only minutes can force tool shutdowns&comma; reduce wafer yields&comma; and trigger costly unplanned downtime&period; Most chip factory backup power systems depend on battery materials &lpar;lithium&comma; cobalt&comma; key chemical precursors&rpar; that are heavily concentrated overseas&period; As a result&comma; a geopolitical or supply chain shock could potentially disrupt a U&period;S&period; semiconductor factory&period; SandboxAQ will build on the progress already made by its AQVolt workflows&comma; which is a frontier AI model for battery chemistry&period; This programmatic area will develop battery chemistries that do not depend on lithium and other materials that have foreign chokepoints&period;<&sol;li>&NewLine;<&sol;ol>&NewLine;<p><b>ReAQT &colon; One Platform Across All Four Programmatic Areas<&sol;b><&sol;p>&NewLine;<p>ReAQT&comma; SandboxAQ&&num;8217&semi;s AI simulation platform&comma; is the foundation for all four material programmatic areas and is built to operate at scale&period; SandboxAQ plans to deepen its investment in ReAQT in order to accelerate the development timeline for new materials discovery&period; ReAQT generates its own physics-grounded training data through high-fidelity simulations&comma; including Density Functional Theory&comma; Molecular Dynamics&comma; and reaction modeling&comma; then trains SandboxAQ&&num;8217&semi;s proprietary Large Quantitative Models &lpar;LQMs&rpar; on that data and integrates them directly into Design-Make-Test workflows&period; Because LQMs learn from physical laws and real-world data&comma; they deliver accurate predictions about materials that have not been previously synthesized&comma; giving researchers a reliable map of what is possible before committing to expensive lab work&period;<&sol;p>&NewLine;<p>Dr&period; Stefan Leichenauer&comma; Vice President of Engineering at SandboxAQ&comma; said&colon; &&num;8220&semi;We built ReAQT around an insight that translates directly into competitive advantage&period; The most accurate simulation methods are too slow to search the range of materials that matter at scale&period; Models trained purely on existing data are fast but break down when applied to materials they have never seen&period; ReAQT solves both problems by generating its own high-quality training data grounded in physics&comma; then training our Large Quantitative Models on it&period; The result is a platform that makes reliable predictions about materials&comma; compressing development timelines in ways that shift what is commercially viable&period;&&num;8221&semi;<&sol;p>&NewLine;

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