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- W3195162105 abstract "High-throughput (HTP) methods have become a powerful method for accelerating the research and development of materials. In this work, we present an example of the whole HTP chain for seeking novel Cu–Sn–S ternary thermoelectric materials including three parts: HTP synthesis, HTP characterization, and HTP analysis. First, the modified diffusion-couple HTP synthesis method is utilized to obtain a bulk sample with nine different raw material (CuS and SnS) ratios. Then, each segment with a fixed raw material ratio is characterized by scanning electron microscopy, and 11 backscattered electron images are taken (99 in total). Finally, we propose two different strategies to classify the different areas in the 99 backscattered electron images and further identify the different phases. The first strategy applies an active learning loop with a fully connected neural network aiming at fast and automated image segmentation. The second strategy employs an unsupervised clustering method to find potentially overlooked compounds without prior knowledge. Two interesting phases Cu7Sn3S10 and Cu1.6S are found and further analyzed, and the former is characterized to be a potential thermoelectric compound with a zT of over 0.6." @default.
- W3195162105 created "2021-08-30" @default.
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- W3195162105 date "2021-08-25" @default.
- W3195162105 modified "2023-10-17" @default.
- W3195162105 title "Accelerating the Discovery of Cu–Sn–S Thermoelectric Compounds via High-Throughput Synthesis, Characterization, and Machine Learning-Assisted Image Analysis" @default.
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- W3195162105 doi "https://doi.org/10.1021/acs.chemmater.1c01856" @default.
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