Cobalt (Co) is the key constituent of hard metal composites that are indispensable for advanced machining tools. Despite its use for over one hundred years, there is a strong interest in finding equivalent alternatives.
The main driving forces for Co substitution are potential health hazards, uncertainties associated with the location of Co deposits, and rising prices due to demand in competing applications. The need for increased sustainability and efficiency requires a wider application of computer-driven materials design. The advance of modelling techniques ranging from quantum mechanical calculations to machine learning approaches enables not only to screen effectively the broad chemical space of potential candidate materials but also to assess their thermodynamic stability and properties of interest.
In the proposed project, we intend to combine quantum mechanical computations and a machine-learned atomic cluster expansion (ACE) to obtain detailed understanding of the ternary system Co-W-C at the atomic scale. ACE will be employed in large-scale molecular dynamics and Monte Carlo simulations to study thermodynamic, kinetic and mechanical properties relevant to real applications. The results will provide crucial insights into the uniqueness of cobalt as a binder material, which is still not fully understood, and facilitate search for an alternative material.
Sandvik Coromant, UK;
Ruhr-Universität Bochum, Germany.