Swift and significant gains against climate change require the creation of novel, environmentally benign, and energy-efficient materials. One of the richest veins researchers hope to tap in creating such useful compounds is a vast chemical space where molecular combinations that offer remarkable optical, conductive, magnetic, and heat transfer properties await discovery. But finding these new materials has been slow going. "While computational modeling has enabled us to discover and predict properties of new materials much faster than experimentation, these models aren't always trustworthy," says Heather J. Kulik PhD '09, associate professor in the departments of Chemical Engineering and Chemistry. "In order to accelerate computational discovery of materials, we need better methods for removing uncertainty and making our predictions more accurate."

The School of Engineering has announced that MIT has granted tenure to eight members of its faculty in the departments of Chemical Engineering, Electrical Engineering and Computer Science, Materials Science and Engineering, Mechanical Engineering, and Nuclear Science and Engineering. "This year's newly tenured faculty are truly inspiring," says Anantha Chandrakasan, dean of the School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. "Their work as educators and scholars has shown an incredible commitment to teaching and research -- they have each had a tremendous impact in their fields and within School of Engineering community." This year's newly tenured associate professors are: Mohammad Alizadeh, in the Department of Electrical Engineering and Computer Science and the MIT Computer Science and Artificial Intelligence Laboratory, focuses his research in the areas of computer networks and systems. His research aims to improve the performance, robustness, and ease of management of future networks and cloud computing systems.

When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system. As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks. The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet PhD '19, Sahasrajit Ramesh, and graduate student Chenru Duan.

In recent years, machine learning has been proving a valuable tool for identifying new materials with properties optimized for specific applications. Working with large, well-defined data sets, computers learn to perform an analytical task to generate a correct answer and then use the same technique on an unknown data set. While that approach has guided the development of valuable new materials, they've primarily been organic compounds, notes Heather Kulik Ph.D. '09, an assistant professor of chemical engineering. Kulik focuses instead on inorganic compounds--in particular, those based on transition metals, a family of elements (including iron and copper) that have unique and useful properties. In those compounds--known as transition metal complexes--the metal atom occurs at the center with chemically bound arms, or ligands, made of carbon, hydrogen, nitrogen, or oxygen atoms radiating outward. Transition metal complexes already play important roles in areas ranging from energy storage to catalysis for manufacturing fine chemicals--for example, for pharmaceuticals.