Alán Aspuru-Guzik: "Billions and Billions of Molecules"

Alán Aspuru-Guzik visited the Quantum AI Lab at Google LA on May 12, 2015 and gave this talk: "Billions and Billions of Molecules: Molecular Materials Discovery in the Age of Machine Learning" Abstract: Many of the challenges of the twenty-first century are related to molecular processes such as the generation, transmission, and storage of clean energy, water purification and desalination. These transformations require a next generation of more efficient and ecologically-friendly materials. In the life sciences, we face similar challenges, for example drug-resistant bacterial strains require novel antibiotics. One of the paradigm shifts that the theoretical and experimental chemists needs to embrace is that of accelerated molecular discovery: The design cycles need to be sped up by the constant interaction of theoreticians and experimentalists, the use of high-throughput computational techniques, tools from machine learning and big data, and the development of public materials databases. I will describe three projects from my research group that aim to operate in this accelerated design cycle. First, I will describe our efforts on the Harvard Clean Energy Project (http://cleanenergy.harvard.edu), a search for materials for organic solar cells. I will continue by talking about our work on developing organic molecules for energy storage in flow batteries. Finally, I will describe our work towards the discovery of novel molecules for organic light-emitting diodes. If time permits, I will talk about molecular networks related to the origins of life. Bio: Professor Alán Aspuru-Guzik is currently Professor of Chemistry and Chemical Biology at Harvard University. He began at Harvard in​ 2006 and ​was promoted to Full Professor in 2013. Alán received his B.Sc.​ Chemistry from the National Autonomous University of Mexico (UNAM) in 1999. He received the Gabino Barreda Medal from UNAM. ​He obtained a PhD in Physical Chemistry from the University of California, Berkeley in 2004, under Professor William A. Lester, Jr., he was a postdoctoral scholar in the group of Martin Head-Gordon at UC Berkeley from 2005-2006. In 2009, Professor Aspuru-Guzik received the DARPA Young Faculty Award, the Camille and Henry Dreyfus Teacher-Scholar award and the Sloan Research Fellowship. In 2010, he received the Everett-Mendelsson Graduate Mentoring Award and received the HP Outstanding Junior Faculty award by the Computers in Chemistry division of the American Chemical Society. In the same year, he was selected as a Top Innovator Under 35 by the Massachusetts Institute of Technology Review magazine. In 2012, he was elected as a fellow of the American Physical Society, and in 2013, he received the ACS Early Career Award in Theoretical Chemistry. He is associate editor of the journal Chemical Science. Professor Aspuru-Guzik carries out research at the interface of quantum information and chemistry. In particular, he is interested in the use of quantum computers and dedicated quantum simulators for chemical systems. He has proposed quantum algorithms for the simulation of molecular electronic structure, dynamics and the calculation of molecular properties. He recently has proposed two new approaches for quantum simulation: the variational quantum eigensolver and the adiabatic quantum chemistry approach. He also proposed the demon-like algorithmic cooling algorithm. He has studied the role of quantum coherence in excitonic energy transfer in photosynthetic complexes. Alán has been involved as a theoretician in several experimental demonstrations of quantum simulators using quantum optics, nuclear magnetic resonance, nitrogen vacancy centers and recently superconducting qubits. ​Alán develops methodology for the high-throughput search of organic materials, especially organic materials. This has led to his discovery of candidate molecules for high mobility organic semiconductors, organic flow battery molecules and high-performance molecules for organic light-emitting diodes. Alan is very interested in the interface of machine learning and material discovery and has carried out the largest set of quantum chemistry calculations to date.