Two notoriously complex chemical structures, previously beyond the reach of even the most powerful computers, have now been successfully modeled thanks to the NVIDIA Blackwell architecture and adapted software. An international collaboration, including researchers from NVIDIA, SandboxAQ and the Wigner Research Centre in Hungary, achieved this breakthrough by combining the Density Matrix Renormalization Group (DMRG) numerical method with the capabilities of GPU supercomputers. The team specifically solved for the structures of FeMoco, a catalyst crucial for fertilizer production, and cytochrome P450, a vital liver enzyme, demonstrating a new level of computational feasibility. “Our study shows that AI-oriented hardware can do more than provide speed—it can also power chemically accurate, strongly correlated quantum chemistry at the frontier of what is computationally feasible,” said Sotiris Xantheas, a computational chemist at Pacific Northwest National Laboratory and study author. This advance, supported by the Department of Energy’s Scalable Predictive methods for Excitations and Correlated phenomena initiative, promises to accelerate the design of new catalysts and materials.
NVIDIA Blackwell Architecture Enables Accurate Quantum Chemistry
These two enzymes, critical to nitrogen conversion in fertilizer production and liver function respectively, serve as benchmarks for evaluating the efficacy of computational chemistry techniques, and their resolution demonstrates a substantial advancement in the field. The research, recently published in the Journal of Chemical Theory and Computation, hinges on a mixed-precision approach, strategically balancing computational speed with chemical accuracy. This breakthrough wasn’t simply about faster processing; it’s about achieving a level of precision previously unattainable. Örs Legeza, of Wigner and the Technical University of Munich, and the study’s senior author, emphasized the practical implications of this approach: “By demonstrating that mixed-precision DMRG with emulated FP64 can reach chemical accuracy for challenging active spaces, we’ve opened a practical path to using Blackwell systems for problems in catalysis, bioinorganic chemistry, and materials science that were previously far harder to access.” This combination of advanced hardware and sophisticated algorithms promises to accelerate materials discovery and catalyst design through computer simulation.
FeMoco & Cytochrome P450 Solved via Mixed-Precision DMRG
Researchers have surmounted longstanding obstacles in quantum chemistry, successfully modeling two notoriously complex chemical structures, FeMoco and cytochrome P450, using a combination of advanced hardware and innovative software techniques. These molecules have long served as benchmarks for computational methods, representing significant challenges due to their intricate electronic structures and the sheer computational expense of accurately simulating them; until recently, even the most powerful computing platforms struggled to provide reliable results. The international collaboration, involving scientists from NVIDIA, Sandbox AQ, and several research institutions, demonstrated that NVIDIA Blackwell architecture, paired with adapted algorithms, could deliver both speed and the necessary precision for these calculations.
“This study shows that NVIDIA Blackwell hardware can not only deliver on the frontier of AI but also impact the physical economy by developing new materials purely in silico,”
Adam Lewis, Head of Innovation, AI Sim, at SandboxAQ
