Computing Tools Speed Search for New Porous Materials

A team of researchers from Lawrence Berkeley National Laboratory and the University of California at Berkeley are building a suite of computational tools to help in the search for new porous materials. These synthetically created materials can play key roles in physical and chemical processes, including petroleum refinement, water softening and in separations. One class, known as zeolites, has a commercial impact of about $350 billion annually.

Berkeley Lab researchers Maciej Haranczyk (left), Chris Rycroft (center) and James Sethian (right) created a suite of computing tools that speed the identication of synthetic porous materials used in energy production, separation membranes and other industrial and scientific applications.

The properties of porous materials can be better understood by studying the voids within—the empty spaces inside the materials—where their building blocks are exposed to penetrating guest molecules. But finding the right new materials can require researchers to investigate millions of structures, which can’t be done manually.

Examples of periodic unit cells of zeolites, with oxygen and silica atoms shown in red and tan, respectively. Isosurfaces (green) represent the boundary of void spaces accessible to a probe molecule, with lighter green denoting the inside of pores. (Select image to enlarge.)

In an article published in the October 2011 issue of SIAM News, chemist Maciej Haranczyk and applied mathematicians Chris Rycroft and James Sethian describe the suite of tools theycreated to speed up the evaluation of new materials. SIAM News is the monthly newsmagazine of the Society for Industrial and Applied Mathematics.

“We have used these tools to perform high-throughput analysis of millions of materials and to determine the accessibility of their void spaces to guest molecules,” the authors wrote. “The tools capitalize on a host of state-of-the-art advances in mathematics, computational algorithms and hardware and software breakthroughs in high-performance computing, in particular parallel processing on multicore CPUs and GPUs.”