Laura and graduate student Daniel King collaborated with the group of Max Delferro at Argonne National Laboratory to develop a better method for finding novel MOF catalysts, which have the potential to speed up industrially relevant chemical reactions. Increasing the efficiency of catalysts is critical for developing sustainable solutions and promoting decarbonization. The group used machine learning algorithms combined with high-throughput experimentation to screen different metals, temperatures, and pressures applied to the MOF NU-1000 for catalytic activity. After 2,000 reactions, the collaboration used this process to ultimately improve the yield of these chemical reactions from 0.4 percent to 24.4 percent. Read the article here.