We use computer simulations and Machine Learning approaches for the investigation of Cu-Ni-Si-Cr alloys. Quantum-mechanical and atomistic Molecular Dynamics (MD) calculations are performed in order to unravel the structural, electronic, and mechanical properties of these alloys. We begin at the lowest scale with the investigation of a number of different Cu-Ni-Si-Cr alloy configurations in which both the chemical composition, size, as well as the crystallographic arrangement of precipitates will be scanned. Our results are tested against available literature data. The influence of single impurities or clusters of impurities on the structural details and the electronic characteristics of the alloys are assessed. Transport calculations further give insight into the electronic conductivity of these alloys. Structural data and energetics from this scale are used to generate Machine Learning potentials. The latter are used in MD simulations for the calculation of the mechanical properties of the Cu-Ni-Si-Cr alloys.
The aim of this project is to provide a framework for the investigation of Cu-Ni-Si-Cr alloys based on computer simulations and data-integrated algorithms. Our approach will shed light into the most stable precipitate phases and provide insight in understanding the mechanisms responsible for enhancing the strength and electrical conductivity in these alloys. The key aspects of this project are the following:
- to calculate the stability of various Cu-Ni-Si-Cr alloys of different compositions, precipitates, and configurations and get access to their electronic properties,
- to calculate the electronic transport and from that the electronic conductivity of these alloys,
- to train an Machine Learning algorithm (with structural data and energies) in order to obtain ML interaction potentials for the alloys,
- use the derived potentials to increase the length scales of our simulations and calculate the mechanical properties of these materials,
- provide a deep understanding and insight into the chemical composition and crystallographic structure of several precipitates and assess their contribution to strengthening and enhancement of the electrical conductivity of Cu-Ni-Si-Cr alloys.
|Project Number||PN 3-7|
|Project Name||Strengthening mechanisms of Cu-Ni-Si-Cr alloys through simulations and Machine Learning potentials|
|Project Duration||November 2019 - May 2023|
|Project Leader||Maria Fyta|
|Project Members||Siegfried Schmauder, Collaborating Applicant
Angel Diaz Carraz, PhD Researcher
|Project Partners||J.Kästner/B.Haasdonk on the use of energy-related data and ML algorithms.
J.Pleiss/N.Hansen on bridging computational scales.
B. Haasdonk on pattern recognition.
I. Steinwart on Neural Network algorithms.
B. Grabowski on Moment Tensor Potentials.