NCSI: Use of ML for Identification of Electronic Properties Trends in Organic Molecular Crystals

Status	Completed
Mentor Name	Bohdan Schatschneider
Mentor's XSEDE Affiliation	Research Allocation
Mentor Has Been in XSEDE Community	4-5 years
Project Title	Use of ML for Identification of Electronic Properties Trends in Organic Molecular Crystals
Summary	Generate Python computer code that will sort organic molecular crystals according to functionalities associated with the molecular components. Then use density functional theory to calculate the electronic properties of said crystals. Then use ML algorithms to establish quatitative structure properties trends in these materials.
Job Description	In this position, the student will write a Python code that will use SMILES input codes to generate an output of the functional groups present on the molecular components. The student will then perform high-throughput DFT calculations in the ORCA and Materials software suites in order to establish the electronic properties (e.g. HOMO-LUMO gap, band gap, polarizability, etc...). The structural and electronic properties data will then be used to generate quantitative structure properties relationships using the program, "R".
Computational Resources	We will use our Open Science Grid start up allocation to run the ORCA DFT calculation and possible to run R if the computational power is needed.
Contribution to Community
Position Type	Intern
Training Plan	Student must be competent in coding in Python. The student picked for this project is current a computer science major that is well versed in Python.
Student Prerequisites/Conditions/Qualifications
Duration	Semester
Start Date	01/20/2020
End Date	05/30/2020