By turning a capability once reserved for specialized computational teams into a self-service product on Amazon SageMaker, SandboxAQ extends its Large Quantitative Models into a market that underpins the majority of the world’s manufactured goods 

PALO ALTO, Calif., Aug. 25, 2026 /PRNewswire/ — SandboxAQ today announced that AQCat, its Large Quantitative Model (LQM) for catalyst discovery, is now generally available in AWS Marketplace and deployable through Amazon SageMaker. The release makes one of the company’s most commercially significant scientific tools available on demand to materials innovators, industrial giants, and discovery teams, who can now run it directly inside their own AWS environment, with no specialized computational scientists or custom code required.

Catalysts are the hidden engine of the physical economy. They are essential to the production of more than 80% of all manufactured goods and over 90% of all commercially produced chemicals, from fertilizer and fuel to fabric, glass, and plastics. Yet the process of discovering better catalysts has remained slow and costly, with conventional laboratory methods typically able to evaluate fewer than 100 candidates per week, a pace that confines most innovation to incremental improvements on existing materials.

AQCat removes that bottleneck. Trained on 13.5 million high-fidelity quantum chemistry calculations spanning 47,000 catalyst systems, it predicts the key properties that determine catalyst performance at up to 20,000 times the speed of conventional physics-based methods while approaching their accuracy. For chemical leaders, that means screening and ranking vast numbers of candidate materials computationally, then reserving costly laboratory and supercomputing work for only the strongest leads. This allows users to compress R&D timelines and de-risk investment across the pipeline.

Making AQCat generally available in AWS Marketplace marks a shift from bespoke scientific engagements to scalable, self-service commercial distribution. Customers can subscribe and deploy within infrastructure they already use and trust, keeping every calculation inside their own AWS account for security and governance. The launch extends a cloud go-to-market strategy that also spans Anthropic, where SandboxAQ offers users access to AQCat via MCP. 

“Highly efficient machine learning interatomic potentials such as AQCat will rapidly accelerate the evaluation of promising new materials and deepen our understanding of their complex transformations,” said Julia Yang, Assistant Professor, School of Chemical and Biomolecular Engineering, Georgia Institute of Technology.

“Catalysis touches nearly everything the global economy produces, yet the tools to discover better catalysts have barely changed in decades,” said Jack Hidary, CEO of SandboxAQ. “By making AQCat available in AWS Marketplace, we’re putting a capability that once required specialized teams and supercomputers into the hands of any enterprise research team, on infrastructure they already trust. This is how we scale our Large Quantitative Models from breakthrough science into everyday commercial impact.”

AQCat’s advantage lies in both reach and fidelity. It is built to account for spin polarization, a physical effect essential to accurately modeling widely used industrial metals such as iron, nickel, and cobalt, and it supports six additional industrially relevant elements (barium, cerium, fluorine, lithium, lanthanum, and magnesium) that are absent from other datasets. Together, these capabilities open the door to catalysts and critical-material pathways that older methods could not reach, with direct applications in green hydrogen, sustainable aviation fuel, fertilizer production, and plastics recycling.

AQCat is generally available to AWS commercial customers in North America in AWS Marketplace. To get started, subscribe at AWS Marketplace: AQCat – AI Models for Catalyst Discovery.

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, BNP Paribas, Eric Schmidt, Breyer Capital, Ray Dalio, Marc Benioff, Thomas Tull, Yann LeCun, and others. For more information, visit www.sandboxaq.com.

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SOURCE SandboxAQ