About this Abstract |
Meeting |
MS&T21: Materials Science & Technology
|
Symposium
|
High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond II
|
Presentation Title |
Exploring the Chemical and Structural Phase Space of High Entropy Alloys with Ab Initio Calculations and Machine Learning Potentials |
Author(s) |
Fritz Koermann |
On-Site Speaker (Planned) |
Fritz Koermann |
Abstract Scope |
Progress in combining ab initio simulations, machine learning interatomic potentials and active learning algorithms are discussed enabling the exploration of structural and chemical stability as well as the elastic properties of high entropy alloys. The application of on-lattice machine learning potentials with Monte Carlo simulations for short-range order are discussed on several examples [1]. The structural stability is presented for TiZrNbHfTa alloys and it is found that atomic relaxations are crucial to accurately determine the structural bcc-hcp phase stability [2]. To distinguish the bcc and omega phase in these complex alloys a structural descriptor is proposed [2]. Utilizing this descriptor in combination with machine learning techniques and molecular dynamics simulations, the temperature-dependent structural-stability and elastic properties for TiZrHfTa alloys are presented, revealing strong intrinsic correlations.[1] npj Comp. Mat. 5, 55 (2019). [2] npj Comp. Mat. 7, 34 (2021). |