Titlebook: Machine Learning-Augmented Spectroscopies for Intelligent Materials Design; Nina Andrejevic Book 2022 The Editor(s) (if applicable) and Th

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,Data-Efficient Learning of Materials’ Vibrational Properties, macroscopic functionalities. While this question is historically addressed through a combination of structure and property characterization, theory, and calculation, machine learning methods guided by crystalline symmetry constraints may provide an alternate route. In this chapter, we demonstrate t

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Machine Learning Spectral Indicators of Topology,n used to identify thousands of candidate topological materials, experimental determination of materials’ topology often poses significant technical challenges. X-ray absorption spectroscopy (XAS) is a widely-used materials characterization technique sensitive to atoms’ local symmetry and chemical e

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Background,entifying the key challenges that call for data-driven insights. Then, we summarize several existing data-driven methodologies and introduce the fundamental building blocks of neural networks, which are implemented to address the identified challenges.

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onsists largely of essays written from an advocate point of view. In contrast, the participants of this Totts Gap Collo- quium examined disparate data and opinion in the hope of achieving, insofar as possible, reconciliation and synthesis. The dialogue dealt with values and priorities attached to health and h978-1-4615-8839-9978-1-4615-8837-5

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