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• W4379519552 abstract "Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices." @default.
• W4379519552 created "2023-06-07" @default.
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• W4379519552 date "2023-06-01" @default.
• W4379519552 modified "2023-10-05" @default.
• W4379519552 title "Materials cartography: A forward-looking perspective on materials representation and devising better maps" @default.
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