Asking how incorporating new entries for post processing using MaterialsProjectCompatibility to correct the energies to account for well-known DFT errors, e.g., in the sulfur binding energy.

[Varsha549]

I wanted to use MaterialsProjectCompatibility to postprocess the MP data, vasprun.xml output file from our VASP calculations, and computed entries of O/S substituted Li-P-O tenary compounds in order to generate a phase diagram and an Electrochemical Stability Window.
In the code that follows:

with open("aimd_data/lpo_entries.json") as f:
lpo_data = json.load(f)
lpo_entries = [ComputedEntry.from_dict(d) for d in lpo_data]

as fallows lpo_entries.json file that has been utilized. The question is: How was this file created, and what computing method was applied in this case? Assist me with the same.

notebooks/2016-09-08-Data-driven First Principles Methods for the Study and Design of Alkali Superionic Conductors Part 2 - Phase and Electrochemical Stability.ipynb

lpo_entries.json

[DanielYang59]

Hi @Varsha549 thanks for reaching out to me.

How was this file created

I assume you refer to the lpo_entries.json file? It's a serialized ComputedEntry stored as a JSON file, to create such file (this is partly demonstrated in the notebook):

# Create the `ComputedEntry` from a VASP calculation
vasprun = Vasprun("aimd_data/vasprun.xml")
entry = vasprun.get_computed_entry()

# Serialize to a dict and write it to a file
with open("filename.json", "w") as file:
    json.dump(entry.as_dict(), file)

what computing method was applied in this case?

I do not really know as a ComputedEntry doesn't contain full details of a calculation. I would suggest reading the paper related to this notebook and reach out to the corresponding author when necessary.