1) Software:
a) A main script from which specified ML models and material descriptors can be deployed on the computing clster. This script will parallelize the training of the ML models and marshal the necessary compute resources.
b) Individual functions/scripts/classes implementing each ML model
c) Scripts to parse material structure files and produce material descriptors
d) Scripts to compare ML model and descriptor pairs and analyze their efficacy
2) A report evaluating the efficacy of a variety of machine learning models and machine descriptors in predicting new material properties.
{Empty}
Graduate student studying physics with further background in computer science, machine learning, and high performance computing. (Michael Butler, UMaine Orono)
{Empty}
Practical applications
{Empty}
University of Maine Orono
105 Bennett Hall
Orono, Maine. 04469
NE-University of Maine
01/01/2019
No
Already behind3Start date is flexible
{Empty}
{Empty}
{Empty}
{Empty}
{Empty}
{Empty}
{Empty}
A module on implementing machine learning models on a HPC cluster.
The discovery of a novel, improved material descriptor or machine learning model would have a very high probability of being published.
The student will learn how to build and efficiently train large, robust machine learning models in a distributed HPC environment.
{Empty}
The Cyberteam will learn how to better support a distributed machine learning environment so that future researchers will have an easier time writing and deploying such software.
Training models will require tens - hundreds of hours of HPC time. Further validation of those models should take a comparable amount of time to that of training. CUDA capable nodes would be helpful, but are not necessary.
{Empty}