These molecular worms are able to change shape during such a voyage, helping them to analyze the void space in the structures of porous materials, and to map areas that other probes can’t access.


The research, by Maciej Haranczyk and James Sethian from the Berkeley National Laboratory and the Department of Mathematics at the University of California, Berkeley, and published in the Proceedings of the National Academy of Sciences (DOI:10.1073/pnas.0910016106), looked at new ways of assessing the passage of a molecule through a chemical system. Such a system, with its multiple channels and cages, is critical to catalysis and other chemical processes such as gas separations.


Rather than rely on manual inspection or human-driven queries, they developed a computational methodology to automatically steer a probe inside a material, examining all possible nooks and crannies to build a virtual map of the inner landscape. Using mathematical algorithms at the heart of path planning and robotic navigation, they were able to construct “molecular worms” from blocks connected by flexible links, rather than the traditional treatment of molecules as hard spheres with fixed radii. The molecular worms ferreted out the inner geometry, allowing a more accurate picture of how that molecule can navigate through a given chemical labyrinth.


The molecular worm algorithm was first tested on zeolite materials, which have been popular for many years as chemical catalysts, membranes for separations and water softeners. Their molecular worm algorithm now makes it possible to screen large libraries of structures for new classes and types of materials, with the goal of focusing additional labor on the most promising candidates. One current application is the search for materials that can capture carbon emissions before they enter the atmosphere.


Their work is part of a larger methodology they are developing to apply information-based techniques to new materials. Similar to virtual screening to guide and support experimental high-throughput analysis for drug design, their goal is develop and use advanced mathematical and computational algorithms as part of the material discovery pipeline.