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MolStat provides tools for understanding and interpreting single-molecule behavior. Single-molecule experiments generally cannot determine, let alone control, the microscopic details of the system and, as such, are usually performed many times and interpreted statistically. The key idea to understanding and interpreting these single-molecule measurements is that each measurement samples from a distribution of possible microscopic configurations. The observed data is thus probabilistic—not deterministic— and a combination of probability theory and statistical mechanics can be used to provide meaning to the data. MolStat contains tools relating experimental statistical data to the underlying physical behavior of single molecules.
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MolStat
MolStat provides tools for understanding and interpreting single-molecule behavior. Single-molecule experiments generally cannot determine, let alone control, the microscopic details of the system and, as such, are usually performed many times and interpreted statistically. The key idea to understanding and interpreting these single-molecule measurements is that each measurement samples from a distribution of possible microscopic configurations. The observed data is thus probabilistic—not deterministic— and a combination of probability theory and statistical mechanics can be used to provide meaning to the data. MolStat contains tools relating experimental statistical data to the underlying physical behavior of single molecules.
To date, MolStat has primarily been used to study how molecules conduct electric current.
We have been able to identify the mechanism for rectification through molecules, quantify
key properties (including level alignment and coupling strength), and remove background
noise from experimental data.
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IACS RESEARCHERS
Matthew Reuter (lead PI)
Stephen Tschudi
Chris Cioffi
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Institute for Advanced Computational ScienceStony Brook UniversityStony Brook, NY 11794-5250
Phone: (631) 632-4629