首页|New Machine Learning Findings from Otto Schott Institute of Materials Research Outlined (Prediction of Phase Composition and Process Resilience In Plasma-assisted Hetero-aggregate Synthesis Using a Machine-learning Model With Multivariate Output)
New Machine Learning Findings from Otto Schott Institute of Materials Research Outlined (Prediction of Phase Composition and Process Resilience In Plasma-assisted Hetero-aggregate Synthesis Using a Machine-learning Model With Multivariate Output)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Machine Learning are presented in a new report. According to news reporting from Jena, Germany, by NewsRx journalists, research stated, “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.” Funders for this research include German Research Foundation (DFG), German Research Foundation within its priority program. The news correspondents obtained a quote from the research from the Otto Schott Institute of Materials Research, “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 mix- tures 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 extrapo- lated into a cloud of approximate to 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.” According to the news reporters, the research concluded: “A machine-learning model with multivariate output is used for predicting process performance and parameter resilience of plasma-assisted aerosol deposition of alumina hetero-aggregates. image.” This research has been peer-reviewed.
JenaGermanyEuropeCyborgsEmerging TechnologiesMachine LearningOtto Schott Institute of Materials Research
NETL
NSTL
Wiley
