Quantum Machine Learning

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• Professor Anatole von Lilienfeld, University of Basel
• Wednesday 08 November 2017, 14:15-15:15
• Department of Chemistry, Cambridge, Unilever lecture theatre.

If you have a question about this talk, please contact Lisa Masters.

Many of the most relevant chemical properties of matter depend explicitly on atomistic and electronic details, rendering a first principles approach to chemistry mandatory. Alas, even when using high-performance computers, brute force high-throughput screening of compounds is beyond any capacity for all but the simplest systems and properties due to the combinatorial nature of chemical space, i.e. all compositional, constitutional, and conformational isomers. Consequently, efficient exploration algorithms need to exploit all implicit redundancies present in chemical space. I will discuss recently developed statistical learning approaches for interpolating quantum mechanical observables in compositional and constitutional space.

This talk is part of the Theory - Chemistry Research Interest Group series.

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