Added a initial description of the P2B1 benchmark.

Author: bvanessen

P2B1/README.md

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+## P2B1: Autoencoder Compressed Representation for Molecular Dynamics Simulation Data
+
+**Overview**: Generate automatically extracted features representing molecular simulation data
+
+**Relationship to core problem**: Establish framework for building future tools using learned features
+
+**Expected outcome**: Improvement in the understanding of protein formation and easing of the handling large-scale molecular dynamics output
+
+### Benchmark Specs Requirements
+
+#### Description of the Data
+* Data source: MD Simulation output as PDB files (coarse-grained bead simulation)
+* Input dimensions: ~1.26e6 per time step (6000 lipids x 30 beads per lipid x (position + velocity + type))
+* Output dimensions: 500
+* Latent representation dimension:
+* Sample size: O(10^6) for simulation requiring O(10^8) time steps
+* Notes on data balance and other issues: unlabeled data with rare events
+
+#### Expected Outcomes
+* Reconstructed MD simulation state
+* Output range: automatically learned features that discriminate the data set
+
+#### Evaluation Metrics
+* Accuracy or loss function: L2 reconstruction error
+* Expected performance of a naive method: N/A
+
+#### Description of the Network
+* Proposed network architecture: stacked fully-connected autoencoder
+* Number of layers: 5-8
+
+### Running the baseline implementation
+
+Using virtualenv
+
+```
+cd P2B1
+workon keras
+python __main__.py --train --home-dir=${HOME}/.virtualenvs/keras/lib/python2.7/site-packages
+```