Accelerating Density Functional Theory Simulations Via Machine Learning: The Example of Stress-Corrosion Cracking in Metals
Abstract
The primary goal of the present proposal is to utilize machine learning based force fields to study the mechanical behavior of a variety of elemental metals experiencing highstresses, high temperatures and corrosive environments, in other words, fudamentalstudies of stress corrosion cracking at time and length scales relevant to this phenomenon. Specifically, Al (an FCC metal), Ti (a HCP metal) and W (a BCC metal) willbe studied. An intentionally created crack (i.e., a notch) will be placed in each material, which will be subjected to notch-opening stresses at various temperatures, in order to understand the primary differences between the three chosen metals. Subsequently, oxygen will be introduced in the notch to probe the role of corrosion on the crack propagation behavior. In order to accomplish these goals, several extensions of this force field concept proposed will be necessary, including ethodological developments to handle multiple elements, strategies to optimally choose the initial training set, and strategies to recognize a new environment when such is encountered during the course of a simulation. It is believed that this work will lead to a fundamental understanding of stress-corrosion cracking, a phenomenon that has enormous aerospace and navy relevance, especially in materials such as Ti which hassignificant technological and application relevance.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2017
- Source ID
- N000141712148
Entities
People
- Ramamurthy Ramprasad
Organizations
- Office of Naval Research
- United States Navy
- University of Connecticut