437575 Cheml- a Machine Learning and Informatics Program Suite for the Chemical and Materials Sciences

Monday, November 9, 2015: 3:15 PM
255A (Salt Palace Convention Center)
Johannes Hachmann, Department of Chemical and Biological Engineering, University at Buffalo, SUNY, Buffalo, NY and Mojtaba Haghighatlari, Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, NY

Computational chemistry and materials science are useful assets to study the properties of novel compounds, materials, and reactions. While these investigations are often valuable for the specific systems at hand, their broader impact tends to be more limited. Our research program focuses on merging computational chemistry and materials modeling with Big Data ideas. The virtual high-throughput screening of candidate libraries allows us to generate huge quantum chemical data sets, which can be employed for data-driven discovery. To extract an understanding of the underlying structure-property relationships from these data sets we develop chemical data mining tools, which are assembled in our CheML software suite. These tools state-of-the-art techniques from machine learning, statistical learning, and informatics. We will discuss their utility and the resulting models for the prediction of molecular properties without the need for expensive quantum chemical calculations.

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