AVS 61st International Symposium & Exhibition
    Accelerating Materials Discovery for Global Competitiveness Focus Topic Wednesday Sessions
       Session MG-WeM

Paper MG-WeM6
Structural Descriptors for Hole Traps in Hydrogenated Amorphous Silicon Revealed through Machine Learning

Wednesday, November 12, 2014, 9:40 am, Room 302

Session: Design of New Materials
Presenter: Tim Mueller, Johns Hopkins University
Authors: T. Mueller, Johns Hopkins University
E. Johlin, Massachusetts Institute of Technology
J.C. Grossman, Massachusetts Institute of Technology
Correspondent: Click to Email
The discovery and design of new materials can be accelerated by the identification of simple descriptors of material properties. However the identification of the most relevant descriptors and how they relate to the properties of interest is not always obvious. We demonstrate how machine learning, in the form of genetic programming, can be used to identify relevant descriptors for predicting hole trap depths in hydrogenated nanocrystalline and amorphous silicon. Amorphous silicon is an inexpensive and flexible photovoltaic material, but its efficiency is limited by low hole mobility. We have evaluated 243 structural descriptors of amorphous silicon to identify those that are most indicative of the hole trap depth. Our calculations reveal three general classes of structural features that influence hole trap depth and predict that multiple interacting defects may result in deeper traps than isolated defects. These results suggest a possible mechanism for the Staebler-Wronski effect, in which exposure to light degrades the performance of amorphous silicon over time.