SandboxAQ’s LQMs Accelerate Semiconductor Materials Discovery with AI

SandboxAQ is concentrating its materials discovery efforts on four specific categories: PFAS-free process chemicals, catalysts, rare earth-free magnets, and battery systems, to directly bolster the U.S. semiconductor manufacturing base. The company is deploying a new approach utilizing Large Quantitative Models, or LQMs, which combine physics-based simulation with machine learning in a proprietary architecture designed to overcome the traditionally slow pace of materials discovery. This focus on speed is critical because the ability to rapidly discover, validate, and commercialize new materials will define leadership in the semiconductor industry. “The next era of semiconductor leadership will be shaped not only by device architecture, but by who can invent and scale the materials that make those architectures possible,” said Shalini Sharma, Head of Semiconductor Materials Innovation and Cross-Vertical Strategy at SandboxAQ. “From new catalysts and PFAS-free process chemicals to advanced battery chemistries and high-performance magnets, we are entering a period where materials innovation becomes a defining strategic lever for industrial resilience and technological advantage.”

Large Quantitative Models Accelerate Semiconductor Materials Discovery

SandboxAQ is shifting the approach to semiconductor materials discovery, leveraging a novel method that prioritizes accelerated innovation to address vulnerabilities in the U.S. supply chain. This differs significantly from conventional methods often hampered by protracted timelines. The LQMs function by first generating physics-grounded data, then training specialized AI and physics models on that data, culminating in automated workflows that streamline the entire design, make, test, learn (DMTL) cycle. This integrated system promises to deliver more reliable results at a faster pace. SandboxAQ’s AQCat workflows, for example, utilize 13.5 million high-fidelity chemistry calculations developed in collaboration with NVIDIA to screen catalyst candidates with increased speed, reducing development timelines from months to weeks, a 20,000-fold acceleration over conventional methods. Similarly, the company’s AQVolt workflows are focused on accelerating the development of next-generation battery materials. This emphasis on speed is a strategic imperative.

AQCat Workflows Enable Rapid Catalyst Screening

SandboxAQ is addressing a critical bottleneck in semiconductor manufacturing: the sourcing of specialized catalysts used in the production of ultra-pure gases and precursors essential for depositing each layer of advanced chips. Currently, the formulations and associated process knowledge for these catalysts are overwhelmingly controlled by foreign suppliers, creating a significant vulnerability in the U.S. supply chain. Traditional catalyst discovery methods are proving inadequate to meet the demands of increasingly complex chip designs and the need for rapid innovation. To overcome these limitations, SandboxAQ has developed AQCat workflows, leveraging a novel approach centered around Large Quantitative Models (LQMs). These models combine physics-based simulation with machine learning through a proprietary three-layer architecture, enabling a dramatic acceleration of the screening process. The system was built on 13.5 million calculations, and the implications extend beyond simply shortening development cycles.

By enabling sustainable, cost-efficient, and ultra-pure manufacturing, AQCat workflows aim to address both economic and strategic concerns, bolstering the resilience of the U.S. semiconductor industry and reducing reliance on foreign-controlled supply chains. The company’s focus is on delivering a platform built to deliver reliable answers faster and at greater scale than traditional methods alone.

AQVolt & LQMs Advance Battery and Magnet Alternatives

SandboxAQ is actively addressing vulnerabilities in semiconductor manufacturing by focusing on domestically sourced materials, beginning with advancements in energy storage solutions. Semiconductor fabrication relies heavily on consistent, precisely controlled power; even brief disturbances can halt production and significantly increase costs, highlighting the strategic importance of resilient backup systems. Currently, most of these systems depend on battery materials, lithium, cobalt, and associated chemical precursors, sourced internationally, creating a potential point of failure for U.S. manufacturers. SandboxAQ’s approach diverges from conventional materials discovery through the application of Large Quantitative Models, or LQMs. Beyond batteries, the company is also leveraging LQMs to identify magnet formulations that reduce or eliminate reliance on rare earth elements, currently sourced from largely foreign-controlled supply chains. Disruptions in rare earth magnet availability can delay equipment certification and reduce factory capacity, underscoring the need for domestic alternatives. By compressing discovery timelines from years to weeks, SandboxAQ aims to build a more self-sufficient and globally competitive American semiconductor industry, starting with the foundational materials that power it.