Incumbent 发表于 2025-3-23 12:22:50

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scotoma 发表于 2025-3-23 16:11:25

,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 发表于 2025-3-23 20:51:58

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规范要多 发表于 2025-3-24 00:49:39

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 发表于 2025-3-24 05:12:34

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Mercantile 发表于 2025-3-24 06:44:06

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Kaleidoscope 发表于 2025-3-24 14:32:25

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 发表于 2025-3-24 17:19:20

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 发表于 2025-3-24 21:37:41

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flaggy 发表于 2025-3-25 03:04:07

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