Update README (#26)

README.md

@@ -3,17 +3,20 @@
 PySAGES (Python Suite for Advanced General Ensemble Simulations) is an Python
 implementation of [SSAGES](https://ssagesproject.github.io) with support for GPUs.
 
-**NOTICE**: This is in early stages of development, expect failures and breaking changes.
+**NOTICE**: This is in early stages of development, expect breaking changes.
 
 ## Installation
 
 Currently, there is only support for
-[HOOMD-blue](https://glotzerlab.engin.umich.edu/hoomd-blue), but gradual support for other
-molecular dynamics engines is planned (for instance, for all engines already supported by
-the original SSAGES).
+[HOOMD-blue](https://glotzerlab.engin.umich.edu/hoomd-blue) and
+[OpenMM](http://openmm.org/), but gradual support for other molecular dynamics engines is
+planned (for instance, for all engines already supported by the original SSAGES).
 
-You need to first install HOOMD-blue and additionaly build the [HOOMD-dlpack
-Plugin](https://github.com/pabloferz/hoomd-dlext).
+You first need to install one of the following plugins depending on your molecular
+dynamics engine
+
+ - For HOOMD-blue visit [HOOMD-dlpack Plugin](https://github.com/SSAGESLabs/hoomd-dlext).
+ - For OpenMM go to [OpenMM](https://github.com/SSAGESLabs/openmm-dlext).
 
 PySAGES also depends on [JAX](https://github.com/google/jax/), follow their installation
 guide to set it up.
@@ -26,7 +29,34 @@ GPU support.
 PySAGES provide a straightforward interface to setup Collective Variables and Enhaced
 Sampling methods to you MD simulations. See the documentation to learn more.
 
+Here is an example of how to hook pysages to an OpenMM simulation of alanine dipeptide
+
+```python
+# Your code to define a OpenMM goes first
+
+# Load pysages
+from numpy import pi
+import pysages
+
+# Declare a list of collective variables
+cvs = (
+    pysages.collective_variables.DihedralAngle((4, 6, 8, 14)),
+	pysages.collective_variables.DihedralAngle((6, 8, 14, 16))
+)
+
+# Define a grid if the sampling method requires one
+grid = pysages.Grid(
+    lower = (-pi, -pi),
+    upper = ( pi,  pi),
+    shape = (32, 32),
+    periodic = True
+)
+
+# Declare the sampling method to be used
+sampling_method = pysages.methods.ABF(ξ, grid, N = 100)
+
+# Hook pysages to the simulation
+sampler = pysages.bind(context, sampling_method)
 
-<div align="center">
-  <img src="docs/assets/pysages.png" alt="PySAGES" height="200">
-</div>
+# Now run the simulation for any number of time steps
+```