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The current InterfacialReactivity module in pymatgen.analysis.interface_reactions is not user-friendly. To view the predicted reactions between two compositions, a user can call InterfacialReactivity.plot(), but they are unable to clearly tell which reactions are predicted, as there are no labels on the plot.
To get the reactions themselves, a user has to call InterfacialReactivity.get_kinks() and dissect a zip object that has an arbitrary order. This means every user of the class must essentially write their own custom API to use it.
At the very least, we should implement a Plotly-based plotter for the predicted reactions so that a user can easily see which reactions are predicted. If possible, however, we should refactor the module such that reactions can more easily be obtained.
Previously, I have written my own API for the class, where it is possible to getComputedReaction objects by calling react_interface(). (Currently, the class only returns basic Reaction objects.)
The current
InterfacialReactivity
module inpymatgen.analysis.interface_reactions
is not user-friendly. To view the predicted reactions between two compositions, a user can callInterfacialReactivity.plot()
, but they are unable to clearly tell which reactions are predicted, as there are no labels on the plot.To get the reactions themselves, a user has to call
InterfacialReactivity.get_kinks()
and dissect a zip object that has an arbitrary order. This means every user of the class must essentially write their own custom API to use it.At the very least, we should implement a Plotly-based plotter for the predicted reactions so that a user can easily see which reactions are predicted. If possible, however, we should refactor the module such that reactions can more easily be obtained.
Previously, I have written my own API for the class, where it is possible to get
ComputedReaction
objects by callingreact_interface()
. (Currently, the class only returns basicReaction
objects.)https://github.com/GENESIS-EFRC/reaction-network/blob/main/src/rxn_network/enumerators/minimize.py#L127:L172
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