Science Concentrates: ACS Meeting News METAL-ORGANIC FRAMEWORKS
Aiding computational screening of MOF catalysts By studying a large set of metal-organic frameworks (MOFs), researchers have uncovered a relationship between the energetics of a reactive-site species and the probability of driving methane oxidation in these porous crystalline materials. The discovery provides rules of thumb that can be incorporated into algorithms for screening large numbers of MOFs quickly in search of catalysts that activate methane under moderate reaction conditions. Vast reserves of natural gas, of which roughly 95% is methane, remain untapped because the resource sits in locations from which shipping is too expensive. Researchers have developed methods for converting the gas to liquids, which are easier to transport, but many of those processes require high temperatures and are eco-
nomically viable only at very large scales. Recent studies show that MOFs are promising catalysts for activating methane under mild conditions. But there are tens of thousands of MOFs, making the task of finding the best one daunting. At the American Chemical Society national meeting in Orlando, Florida, last week, Northwestern University’s Andrew S. Rosen reported on an advance that may improve computer-based methods for screening MOFs for promising methane-activation catalysts. Using quantum calculations, Rosen and coworkers found that a MOF’s ability to break the strong C−H bond in methane— the first step in converting the gas to methanol—can be directly related to how easy it is to oxidize the MOF’s surface metal atoms
and form a metal-oxo active site (ACS Catal. 2019, DOI: 10.1021/acscatal.8b05178). The study also uncovered a periodic trend: MOFs with later-transition metals form less stable but more reactive metal-oxo sites than ones with early-transition metals. By combining this new correlation with earlier work on methane activation, this group has developed an equation that can describe both the activity toward methane activation and the ability to form the active site, remarked the University of Delaware’s Dionisios G. Vlachos, a catalysis specialist. This allows chemists to predict a range of suitable catalyst candidates. “Clearly, this study will stimulate interest from computationalists and experimentalists alike.”—MITCH JACOBY
COMPUTATIONAL CHEMISTRY
Quantum layer boosts machine-learning predictions calculations from the algorithms because including them would make calculations take too long. Yaron’s quantum layer uses data predicted by machine learning, like energy machine-learning algorithms. gaps between molecules’ electronic orbitYaron said he wants chemists to be able als, to rebuild quantum chemical knowlto model organic molecules quickly withedge and feed it back into the out sacrificing accuracy. When O predictions. The researchers calculations have to run overtrained their augmented algonight or over a weekend it can HO rithm on more than 12,000 orslow down discovery, he said. 2-Hexenoic acid ganic molecules containing up Typically, machine-learning to seven nonhydrogen atoms, then tested algorithms can predict an unknown molits ability to predict properties of moleecule’s properties after being trained with cules with eight nonhydrogen atoms. data sets that contain the properties of The best version of their model predictthousands or more molecules. Quantum ed molecular energies with a 67% error information can be incorporated into reduction and dipole moments with a these training sets. But Adrian Roitberg 59% error reduction relative to an algoof the University of Florida, who helped rithm without the quantum layer (J. Chem. develop the machine-learning algorithms Theory Comput. 2018, DOI: 10.1021/acs. that Yaron’s group built on, said that his jctc.8b00873).—SAM LEMONICK group and others usually strip quantum
Added algorithms reduce errors in predicting electronic properties Computational chemistry lets chemists predict molecules’ properties without measuring them in the lab. Some of the most accurate computational chemistry tools use quantum chemistry, but these calculations can be time consuming. At the American Chemical Society national meeting in Orlando, Florida last week, David J. Yaron of Carnegie Mellon University described a way to speed up calculation of molecular properties by combining quantum chemistry with machine learning. By adding a quantum chemistry “layer” to a machine-learning algorithm to predict the dipole, atomic charge, and other properties of a molecule, Yaron said, his team was able to reduce calculation errors by as much as two-thirds relative to standard
8
C&EN | CEN.ACS.ORG | APRIL 8, 2019
C R E D I T: A RU N GO PA L A N /N O RT H W EST E R N U.
C&EN Global Enterp 2019.97:8-8. Downloaded from pubs.acs.org by KEAN UNIV on 05/04/19. For personal use only.
Uncovered energetic correlation could help chemists find MOFs that catalyze methane activation
Forming a metal-oxo active site in a MOF is key to converting methane to methanol. C = gray. H = white. O = red. N = blue. Cl = green. Metal = pink.
