SandboxAQ

Today, SandboxAQ announced the close of our Series E round, raising over $450 million in new funding and welcoming a powerhouse group of strategic investors, including Ray Dalio, Horizon Kinetics LLC, BNP Paribas, Google, and NVIDIA. They join a world-class roster of long-term backers—Breyer Capital,Eric Schmidt, T. Rowe Price, Paladin Capital Group, and others—bringing SandboxAQ’s total funding to over $950 million since our 2022 spin-out from Alphabet. These expanded resources strengthen our ability to deliver even more to our customers—advancing the scale and performance of our science-driven Large Quantitative Models (LQMs) to tackle complex challenges across life sciences, advanced materials and chemicals, financial services, navigation, cardiography, cybersecurity, and more. Read the full announcement: https://lnkd.in/dZ2ShXxd

NYSE recognizes SandboxAQ's successful round to drive real-world impact with quantitative AI across critical sectors like healthcare, energy, and defense. This is a signal that Quantitative AI is moving to the center of enterprise and national priorities. The convergence of AI and physics-based modeling is no longer theoretical—it's actionable. With this support, we’re accelerating the deployment of Large Quantitative Models (LQMs) across sectors: - Driving faster drug discovery. - Advancing materials science. - Securing critical infrastructure. - Enhancing defense and healthcare through precision sensing and encryption management. Read the press release: https://lnkd.in/dZ2ShXxd

A new peer-reviewed paper—Orbital Optimization of Large Active Spaces via AI-Accelerators—co-authored by SandboxAQ, NVIDIA, and researchers from leading academic institutions, demonstrates a significant advance in high-accuracy modeling of strongly correlated systems. The study showcases orbital optimization for 82 electrons in 82 orbitals (CAS(82,82)), enabled by GPU-accelerated DMRG combined with CAS-SCF in the ORCA software suite. The result: - Simulations that once took weeks now run in hours - No reliance on supercomputing clusters - High-fidelity results with significantly reduced compute overhead This approach unlocks new capabilities for discovery teams working in materials science, catalysis, and drug discovery, where accurate modeling at scale is essential. Read more about the work and what it makes possible: https://lnkd.in/dGMPbSu8

Quantum chemistry, accelerated. In a recent collaborative study, researchers from SandboxAQ, NVIDIA, Pacific Northwest National Laboratory, and leading academic institutions demonstrated how AI accelerators can significantly reduce compute times for complex quantum chemical systems. 113 electrons. 76 orbitals. One GPU node. Achieving 80× speedup over traditional CPU-based methods, simulations that previously took weeks now complete in hours—without the need for supercomputing clusters or manual tuning. This advancement enables scalable, high-accuracy modeling for applications in drug discovery, catalysis, and materials science, paving the way for more efficient and confident exploration of chemical space. Get the infographic and read the full paper: https://lnkd.in/d93fWrup #AI #HPC #DrugDiscovery #MaterialsScience #SandboxAQ #NVIDIA #PNNL

SandboxAQ has announced a collaboration with NVIDIA to accelerate the next era of scientific discovery and enterprise innovation. Powered by NVIDIA DGX Cloud on Google Cloud, our Large Quantitative Models (LQMs) introduce a powerful new approach to AI—grounded in the laws of physics, chemistry, biology, and math. This enables a level of speed, precision, and reliability that traditional AI can’t match. This new collaboration enables SandboxAQ to unlock critical breakthroughs specifically designed to transform customer outcomes, including: - Up to 4x faster discovery across drug, chemical, and materials pipelines - SandboxAQ's Agentic AI Chemist, an autonomous system that explores millions of chemical pathways to uncover novel compounds - High-fidelity datasets combining chemical and biological simulations to reduce false positives and improve model performance This collaboration builds on recent technical breakthroughs: - 80x acceleration in quantum chemistry using CUDA-accelerated DMRG - A new simulation milestone: 82 electrons in 82 orbitals, doubling the previous benchmark "Our work with NVIDIA accelerates our customers’ ability to innovate and lead,” said Jack Hidary, CEO of SandboxAQ. “With DGX Cloud and our ongoing research, we’re delivering performance and insight that give our customers a clear edge.” Learn more: https://lnkd.in/dMr6Pru3

Join Ang Xiao, Technical Lead of Materials Science at SandboxAQ, at The Battery Show South on April 16 as he shares how SandboxAQ is using Large Quantitative Models (LQMs) to drive practical advances in battery materials research. Drawing on extensive data from raw material suppliers and cell manufacturers, his session will explore how early predictions of battery degradation can improve lifecycle management, reduce costs, and support sustainability goals. He’ll also highlight how graph neural networks outperform traditional models in predicting battery performance—offering a more accurate and scalable approach to energy storage innovation. Catch him at 1:45 pm EDT in the Open Tech Forum.

What if detecting heart disease became simpler, faster, and more accurate? Jack Hidary, CEO of SandboxAQ, explores this future in his latest article for the Forbes Technology Council. He breaks down how quantitative AI and magnetic sensing are creating a new generation of cardiac diagnostics that enable point-of-care magnetic imaging of the heart. These advanced MCG devices deliver rich data and real-time insights into the function of the heart - without complex infrastructure or invasive procedures. The implications for healthcare systems are significant: reduced ER congestion, lower costs, and improved patient outcomes. Read the article: https://lnkd.in/gBc3cYwm

Are your security systems ready for the next phase of digital transformation? Join us at the Accenture Quantum Workshop on April 15, where SandboxAQ will be presenting alongside key partners and innovators. The agenda brings together leaders shaping the convergence of AI, advanced sensors, and encryption management. From modernizing cyber resilience with unified cryptography management to unlocking breakthrough capabilities in navigation, defense, and scientific research, SandboxAQ’s technologies are laying the foundation for secure, intelligent enterprise operations. This event will explore how these innovations are not only shaping the quantum future—but delivering measurable impact today. Reserve your spot here: https://lnkd.in/gfG-nEe6

From LLMs to LQMs: The next evolution of AI is here—and it’s built for the physical world. Stefan Leichenauer joined GenAI.Works' Steve Nouri for a live conversation on why AI must evolve to meet real-world demands. LQMs combine machine learning, physics-based computation, and modular design to solve complex business and scientific challenges that require more than just words. Think: magnetic navigation, next-gen medical devices, accelerated drug discovery for cancer, and materials science innovation. Hear from Stefan, rewatch the webinar here: https://lnkd.in/gc-2y98Z

AQtive Guard has been named one of SC Media’s Best Emerging Technologies in 2025. This recognition highlights the urgent need for unified cryptographic management as enterprises confront rising risks—from AI-powered threats to future quantum decryption. Today, many organizations can’t answer three basic questions: What cryptography are we using? Where is it deployed? Is it secure? AQtive Guard gives security teams greater visibility and control over their cryptographic environments—mapping every key, certificate, and algorithm across the entire IT infrastructure to expose hidden vulnerabilities and automating policy enforcement at scale. Enterprises need strong encryption for Zero Trust initiatives, regulatory compliance and protecting sensitive data. AQtive Guard delivers a centralized solution to discover, manage, and protect all of your cryptographic assets.