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Show Notes
For our first event, Brenda Rubenstein has presented a tutorial on her lab’s approach to storage and computation, making use of the chemical properties of a variety of types of small molecules. This was a real tour-de-force, and is worth a watch. Be sure to listen to our subsequent Q&A session in a couple episodes time!
Abstract: As transistors near the size of molecules, computer engineers are increasingly finding themselves asking a once idle question: how can we store information in and compute using chemistry? While molecular storage and computation have traditionally leveraged the sequence diversity of polymers such as DNA, our team has recently demonstrated that vast amounts of information can also be stored in unordered mixtures of small molecules. In this tutorial, I will begin by explaining this new, more general molecular storage paradigm and how polymers fit into it. I will then describe how our team has married combinatorial chemical synthesis with high resolution spectrometry to experimentally realize this paradigm and store GBs of information in small molecules and metabolites. Lastly, I will end with a discussion of how these storage principles can be combined with machine learning techniques to realize fully molecular neural networks for pattern recognition and image processing. The new paradigm discussed in this tutorial will lend itself to new means of increasing molecular storage capacity and interpreting the many small molecule chemistries that underlie “computing” within the body.
Following on from Brenda’s fantastic tutorial, we chatted with her to get answers to many questions, find out more about her lab’s work, and get her thoughts on the future direction of this approach!
Abstract
As transistors near the size of molecules, computer engineers are increasingly finding themselves asking a once idle question: how can we store information in and compute using chemistry? While molecular storage and computation have traditionally leveraged the sequence diversity of polymers such as DNA, our team has recently demonstrated that vast amounts of information can also be stored in unordered mixtures of small molecules. In this tutorial, I will begin by explaining this new, more general molecular storage paradigm and how polymers fit into it. I will then describe how our team has married combinatorial chemical synthesis with high resolution spectrometry to experimentally realize this paradigm and store GBs of information in small molecules and metabolites. Lastly, I will end with a discussion of how these storage principles can be combined with machine learning techniques to realize fully molecular neural networks for pattern recognition and image processing. The new paradigm discussed in this tutorial will lend itself to new means of increasing molecular storage capacity and interpreting the many small molecule chemistries that underlie “computing” within the body.
Biography
Dr. Brenda Rubenstein is currently the Joukowsky Family Assistant Professor of Chemistry at Brown University. While the focus of her work is on developing new electronic structure methods, she is also deeply engaged in rethinking computing architectures. Prior to arriving at Brown, she was a Lawrence Distinguished Postdoctoral Fellow at Lawrence Livermore National Laboratory. She received her Sc.B.s in Chemical Physics and Applied Mathematics at Brown University, her M.Phil. in Computational Chemistry while a Churchill Scholar at the University of Cambridge, and her Ph.D. in Chemical Physics at Columbia University. Ask her about basketball—you may be surprised!