Abstract: Artificial Intelligence and machine learning techniques have gained tremendous interest in the chemistry community. From the discovery of pharmaceuticals and materials to predicting response properties, the impact of these approaches continue to exceed expectations. Though AI and ML have assisted in chemical discovery and design, there are challenges that remain. In this talk, I will address some of the lessons learned in trying to implement machine learning and smart-object based approaches in our own research portfolio and how to approach mitigating future challenges.
Biography: Andre Clayborne is currently the Interim Chair and Associate professor in the Department of Chemistry and Biochemistry at George Mason University. After receiving his Ph.D. from Virginia Commonwealth University in Chemical Physics in 2009, he held postdoctoral appointments at the University of Jyväskylä (Finland), Argonne National Laboratory, and Kansas State University. He has held faculty appointments at the University of Missouri-Kansas City and Howard University (Washington, D.C.) previously. Dr. Clayborne’s research focuses on understanding the properties of molecules and nanoparticles for materials. A key goal of his research is to accelerate the discovery of materials using computational approaches such as DFT, Data Science, and Quantum Computing.
