"This inverse design approach demonstrates the potential of machine learning and algorithm engineering approaches to challenging problems in materials science," says Kathleen McKeown, director of the Institute for Data Sciences and Engineering and Henry and Gertrude Rothschild Professor of Computer Science. "At the Institute, we are focused on pioneering such advances in a number problems of great practical importance in engineering."
Venkatasubramanian adds, "Discovering and designing new advanced materials and formulations with desired properties is an important and challenging problem, encompassing a wide variety of products in industries addressing clean energy, national security, and human welfare." He points out that the traditional Edisonian trial-and-error discovery approach is time-consuming and costlyit can cause major delays in time-to-market as well as miss potential solutions. And the ever-increasing amount of high-throughput experimentation data, while a major modeling and informatics challenge, has also created opportunities for material design and discovery.
The researchers built upon their earlier work to develop what they call an evolutionary framework for the automated discovery of new materials. Venkatasubramanian proposed the design framework and analyzed the results, and Kumar developed the framework in the context of self-assembled nanomaterials. Babji Srinivasan, a postdoc with Venkatasubramanian and Kumar and now an assistant professor at IIT Gandhinagar, and Thi Vo, a PhD candidate at Columbia Engineering, carried out the comp
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