Exploration, exploitation and understanding of the vast chemical space for functional materials is virtually impossible by conventional manual experimentation and necessitates the integrated deployment of automated experimental and theoretical methods. We therefore employ high-throughput and combinatorial methods in materials design, synthesis, characterization and device building with close integration with corresponding virtual experiments. Recognizing the need to link virtual and physical experiments, we actively develop methods for inter-lab orchestration and ontology-linked data management.
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A Flexible Biorefinery using Machine Learning
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Digitizing Materials (DigiMat) Workshop strengthened the Aalto-TUM strategic partnership
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Science advancements powered by machine learning
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