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

Incumbent

scotoma

,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

companion

规范要多

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

Indicative

Mercantile

Kaleidoscope

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.

NEG

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

CONE

flaggy