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A group of scientists, entrepreneurs, and institutional allies who cooperate to advance molecular machines, applications in energy, medicine, and material science, and long-term progress toward Richard Feynman’s vision of nanotechnology.

Brenda Rubenstein | Computing with Molecules: Storage and Computation Using Small Molecules and Their Reaction Networks
As transistors near the size of molecules, computer engineers are increasingly finding
themselves asking a once idle question: how can we compute using chemistry? In this talk, I
will discuss recent progress my Brown Molecular Informatics team and I have made
demonstrating how mixtures of small, unordered molecules can process information. During the
first portion of this talk, I will illustrate how combinatorial chemical synthesis combined with high
resolution mass spectrometry can be harnessed to store GBs of information in small molecules
and metabolites. I will then turn to describing how basic principles of chemistry, such as mixing,
complementarity, chemical reaction networks, and autocatalysis, can be exploited to realize fully
molecular neural networks for machine learning and image processing. I will end with a
discussion of the challenges molecular computation faces that may be resolved with clever
doses of synthetic and theoretical chemistry, and the connections molecular computation has to
the development of intelligent molecular machines.

Biography:​
Dr. Brenda Rubenstein is currently an Associate Professor of Chemistry and Physics at Brown
University. While much of her group works on electronic structure theory, computational
biophysics, and quantum computing, 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 while a
DOE Computational Science Graduate Fellow at Columbia University.
https://rubenstein.group/

Timecodes
00:00 Introduction to Molecular Machines Group
00:25 Brenda Rubenstein's Background and Research Focus
00:46 Computing with Small Molecules
03:47 Challenges and Opportunities in Molecular Computing
07:18 Molecular Storage Techniques
14:57 High Throughput Molecular Storage
17:42 AI and Molecular Data Analysis
20:14 Applications and Future Directions
23:45 Introduction to Molecular Computation
26:14 Combining Chemicals for Image Recognition
26:33 Training Neural Networks with Chemistry
27:24 Exploring Autocatalytic Reactions
30:41 Implementing Dual Rail Encoding
33:27 Challenges and Future Directions
36:20 Q&A Session
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