| Abstract Scope |
As MXene compositional space grows, both novel synthesis and property optimization remain critically linked to their precursor MAX phases. Machine learning (ML) is a promising technique to predict new possible compositions, and synthesis approaches to yield higher quality MAX and MXenes. By leveraging ML models, novel MAX and MXene discovery can be expedited to reduce time and cost associated with synthesis. However, for these models to be effective, experimental data is needed to train them. Therefore, a study using a high-throughput processing of three double-transition metal solid solution MAX phases, (Ti, Nb)2AlC, (Ti, Ta)2AlC, and (Ti, V)2AlC, optimized sintering temperature and molar ratio effect on phase stability and occupancy. The systematic exploration of different conditions, on both successful and unsuccessful phases, yields crucial data to improve machine learning algorithms for use with other MAX systems for scalable and efficient synthesis. |