A Machine Learning Approach Towards Quantitative Structure-Property Relationships for Metallic Interfaces
Abstract
In the proposed research program, the PI will develop machine-learning tools and will construct reliable reduced-order models for GB energies and temperature-dependent mobilities as a function of the macroscopic five-parameter space. The GB atomistic structure will be quantified using two-point correlation functions and robust statistical models will be built by utilizing dimensionality reduction techniques. Experimentally obtained GB property measurements will be investigated through a regression based analysis, made possible by the development of interpolating basis functions in the five-parameter space of GBs. The basis functions will not only facilitate a comparison of the simulated data with experimental measurements, but will enable the prediction of atomistic structures as a function of GB crystallography. The machine-learning techniques developed in this research program will transform the quantification of GB crystallography-structure-property relationships as they can be easily extended to the analysis of a wide range of material systems, such as multi-component alloys and functional ceramics, and to the quantification of complex properties, such as diffusivity, conductivity, corrosion resistance, and defect-interface interactions.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 02, 2017
- Source ID
- FA95501710145
Entities
People
- Srikanth Patala
Organizations
- Air Force Office of Scientific Research
- North Carolina State University
- United States Air Force