Prediction of Phase Composition and Process Resilience in Plasma‐Assisted Hetero‐Aggregate Synthesis using a Machine‐Learning Model with Multivariate Output

Abstract

The synthesis of nanoscale particles and particle aggregates from liquid or gaseous precursors is affected by a variety of trade‐off relations, for example, in terms of product composition, yield, or energy efficiency. Machine‐supported process evaluation and learning (ML) of these relations enables optimization strategies for advanced material processing. Such a workflow is demonstrated on the example of plasma‐assisted aerosol deposition (PAAD) of alumina powders. Depending on processing conditions, these powders comprise of hetero‐aggregate mixtures of crystalline and amorphous polymorphs. Process optimization toward a specific target composition calls for ML approaches. For this, a sufficiently large and consistent dataset of PAAD input (processing) and output (product) parameters is initially generated by real‐world processing, and subsequently extrapolated into a cloud of ≈106 input‐output parameter matrices using Gaussian process regression with multivariate output and input‐output feature analysis. It is subsequently demonstrated how not only the phase composition of the obtained alumina powders, but also product resilience to variations in specific processing parameters, or – as a perspective – the energy efficiency of material processing can be predicted.