Speaker: Alán Aspuru-Guzik, Harvard University
Title: Billions and billions of molecules: Exploring chemical space with classical and quantum computers
Many of the challenges of the twenty-first century are related to molecular processes such as the generation and storage of clean energy, water purification and desalination. These transformations require a next generation of more efficient, chemically stable, and non-toxic materials. Chemical space, the space of all possible synthesizable molecules, is practically infinite and promises to have relevant candidate functional molecules to address these challenges. One of the main goals of my research group is to develop understanding and tools for the exploration chemical space to accelerate the discovery of organic materials. One of the challenges for computational materials design is that the solution of the many-body Schrödinger equation is only practical using approximate methods on classical computers. Our design cycle is sped up by the constant interaction of theoreticians and experimentalists, the use of high-throughput computational techniques, machine learning, and the development of specialized data tools. We have had recent successes in theoretically predicting and experimentally confirming in record times top performers in the areas of organic electronics, organic flow batteries and organic light-emitting diodes. In this talk, I will briefly overview our molecular discovery process and discuss our progress in developing and employing machine learning tools to accelerate the discovery process and gain insight on chemical space. I will focus on our recent work on autoencoders and generative adversarial networks for optimizing molecular materials targets. I will then briefly discuss our group’s progress in developing numerically exact algorithms for quantum computers for solving chemical problems. Turning back to machine learning, but now in the context of quantum computers, I will end the talk by briefly overviewing our recent development of a quantum autoencoder and a quantum artificial neuron unit, the basic building block for quantum neural networks.
Alán Aspuru-Guzik is currently Professor of Chemistry at Harvard University, where he started his independent career in 2006 and was promoted to Full Professor in 2013. Alán received his B.Sc. from the National Autonomous University of Mexico (UNAM) in 1999. He obtained a PhD from the University of California, Berkeley in 2004, where he also was a postdoctoral scholar from 2005-2006. Aspuru-Guzik carries out research at the interface of quantum information and chemistry. In particular, he pioneered the development of algorithms and experimental implementations of quantum computers and dedicated quantum simulators for chemical systems. He has studied the role of quantum coherence in excitonic energy transfer in photosynthetic complexes. He has accelerated discovery by means of computation for organic semiconductors, organic photovoltaics, organic batteries and organic light-emitting diodes. Amongst other recognitions, Aspuru-Guzik has been recipient of the DARPA Young Faculty Award, the Sloan Research Fellowship, and was selected as a Top Innovator under 35 by the MIT Technology Review. He is a fellow of the American Physical Society and in 2013 he received the Early Career Award in Theoretical Chemistry from the American Chemical Society.
For additional information contact Eyal deLara