Argonne lab demos predicative modeling to boost materials discovery for fuels

April 28, 2015 |

In Illinois, with access to supercomputing resources at the Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, a research team from the University of Minnesota and Rice University has demonstrated a predictive modeling capability that can help accelerate the discovery of new materials to improve biofuel and petroleum production.

The findings, recently published in Nature Communications, present a tool that could lead to more efficient processes in the biofuel and petrochemical industries, while reducing the time and cost of associated laboratory research and development efforts.

The materials of interest are called zeolites, which are used as both molecular sieves and catalysts to help make fuels and chemical feedstocks. To date, more than 200 types of zeolites have been synthesized and more than 330,000 potential zeolite structures have been predicted based on previous computer simulations.

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Category: Research

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