Tackling complex simulation with mathematical methods

Örs Legeza, TUM-IAS Alumni Fellow and Professor at the Wigner Institute in Budapest, solved difficult quantum-chemistry problems involving large numbers of interacting electrons. These types of problems are especially interesting to researchers seeking to understand and ultimately emulate complex energy conversions, such as catalysis and semiconductor behavior. Örs Legeza specializes in developing and using a numerical method called Density Matrix Renormalization Group (DMRG). As a result, the most demanding calculations in quantum chemistry can now be solved with graphics processing unit (GPU) supercomputers. 

An international research team collaborated on his project: computional chemists from NVIDIA, Sandbox AQ, the Wigner Research Centre in Hungary, the Institute for Advanced Study of the Technical University of Munich in Germany, and the Department of Energy’s Pacific Northwest National Laboratory.

To demonstrate the mixed-precision method, the research team chose to model the structures of the active compounds FeMoco, which catalyzes the conversion of atmospheric nitrogen to ammonia, a key component of fertilizer, and cytochrome P450, an important liver enzyme. These two enzymes are seen as benchmarks for computational methods and exemplify the kinds of practical chemical structures researchers would like to solve. But until now, solving these types of structures has proven too complex and time-consuming for even the most advanced computing platforms. 

His research team solved the problems by combining DMRG with techniques emulating FP64 arithmetic through reduced precision compute resources developed for handling AI workloads. Their strategy involved tolerating less precision where it wasn’t necessary and reserving high precision for the calculations that demand it. The result was the first quantum chemistry calculation using FP64 emulation that achieved chemical accuracy.

“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 next-generation Blackwell systems for problems in catalysis, bioinorganic chemistry, and materials science that were previously far harder to access,” said Legeza.

The research team successfully demonstrated that it’s possible to creatively combine leading-edge GPU technologies with advanced scientific computing methods. Chemists can now benefit from the massive computational capabilities available in GPUs for tackling today’s thorniest scientific computing challenges.

See the Press Release and more information on Örs Legeza’s research.