Machine-learning helps sort out massive materials' databases

EPFL and MIT scientists have used machine-learning to organize the chemical diversity found in the ever-growing databases for the popular metal-organic framework materials. Metal-organic frameworks (MOFs) are a class of materials that contain nano-sized pores. These pores give MOFs record-breaking internal surface areas, which can measure up to 7,800 m² in a single gram of material. As a result, MOFs are extremely versatile and find multiple uses: separating petrochemicals and  gases ,  mimicking DNA , producing hydrogen , and removing  heavy metals ,  fluoride anions , and even  gold  from water are just a few examples. Because of their popularity, material scientists have been rapidly developing, synthesizing, studying, and cataloguing MOFs. Currently, there are over 90,000 MOFs published, and the number grows every day. Though exciting, the sheer number of MOFs is actually creating a problem: "If we now propose to synthesize a new MOF, how can we know if it is truly a new structure and not some minor variation of a structure that has already been synthesized?" asks Professor Berend Smit at EPFL Valais-Wallis, which houses a major chemistry department.