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| -rw-r--r-- | README.md | 8 |
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@@ -2,8 +2,12 @@ This is an exploration of a representation (or descriptor), based on the Lennard-Jones potential, for use in prediction of atomization energies (and possibly other properties) of molecules using Machine Learning (ML). -An implementation of existing representations, this new representation and most of the ML routine is created from the, mostly, ground up. +An implementation of existing representations (for now only Coulomb matrix), this new representation and most of the ML routine is created from the, mostly, ground up. -The dataset used is the QM7 dataset obtained from the [QML tutorial repository](https://github.com/qmlcode/tutorial). On the other hand, the periodic table of elements data was retrieved from [this handy Gist](https://gist.github.com/GoodmanSciences/c2dd862cd38f21b0ad36b8f96b4bf1ee). +## Data used + +* The *QM7* dataset obtained from the [QML tutorial repository](https://github.com/qmlcode/tutorial). This can also be retrieved from the [quantum-machine webpage](http://www.quantum-machine.org/datasets/), but for its use with python, the dataset given by QML is more useful. +* The *QM9* dataset is obtained from the [quantum-machine webpage](http://www.quantum-machine.org/datasets/), but it's slightly modified for its use with python. +* On the other hand, the *periodic table of elements* data was retrieved from [this handy Gist](https://gist.github.com/GoodmanSciences/c2dd862cd38f21b0ad36b8f96b4bf1ee). *NOTE*: This is not supposed to be a python package (for now). |