Ideal candidate: scientists skilled in Density Functional Theory and proficient in python.
The aim of this task is to create a simple python package that implements automatic basis set selection mechanism for a quantum chemistry engine.
- automatically find the basis set delivering a particular precision, passed as argument (eg. within 0.01% from reference)
- use either experimental data or higher-fidelity modeling results (eg. coupled cluster) as reference data
- example properties to converge: HOMO-LUMO gaps, vibrational frequencies
- mine reference data for use during the project
- correctly find a basis set that satisfies a desired tolerance for a set of 10-100 molecules, starting from H2, as simplest, up to a 10-20-atom ones
- modular and object-oriented implementation
- commit early and often - at least once per 24 hours
We leave exact timing to the candidate. Must fit Within 5 days total.
As a user of this software I can start it passing:
- molecular structure
- reference datapoint
- tolerance (precision)
as parameters and get the basis set that satisfies the tolerance criterion.
- create an account at exabyte.io and use it for the calculation purposes
- suggested modeling engine: NWCHEM or SIESTA