標(biāo)題: Titlebook: Machine Learning-Augmented Spectroscopies for Intelligent Materials Design; Nina Andrejevic Book 2022 The Editor(s) (if applicable) and Th [打印本頁(yè)] 作者: choleric 時(shí)間: 2025-3-21 19:12
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作者: 修剪過(guò)的樹(shù)籬 時(shí)間: 2025-3-21 23:22 作者: hemophilia 時(shí)間: 2025-3-22 02:44
Nina AndrejevicNominated as an outstanding PhD thesis by Massachusetts Institute of Technology.Introduces machine learning methods for neutron and photon scattering and spectroscopy.Identifies spectral signatures of作者: ASTER 時(shí)間: 2025-3-22 06:36 作者: 無(wú)能性 時(shí)間: 2025-3-22 11:47
Conclusion and Outlook,In this chapter, we summarize the primary contributions of this thesis work and offer a short perspective on the possible extensions of each study, concluding with a discussion of outstanding challenges and emerging approaches in the field.作者: 職業(yè) 時(shí)間: 2025-3-22 14:58
https://doi.org/10.1007/978-3-031-14808-8machine learning for materials characterization; machine learning Raman spectra; machine learning neut作者: 得體 時(shí)間: 2025-3-22 19:23 作者: exhibit 時(shí)間: 2025-3-23 00:08 作者: Brocas-Area 時(shí)間: 2025-3-23 03:18
Nina Andrejevicrked control and multi-agent systems.Written by experts in tThis authored monograph presents a study on fundamental limits and robustness of stability and stabilization of time-delay systems, with an emphasis on time-varying delay, robust stabilization, and newly emerged areas such as networked cont作者: 哺乳動(dòng)物 時(shí)間: 2025-3-23 09:35
Introduction,ctural and dynamical properties at atomic to mesoscopic length scales. As advances at scientific user facilities enable the collection of ever larger data volumes in higher-dimensional parameter spaces, the design, analysis, and interpretation of such experiments becomes both increasingly valuable a作者: Incumbent 時(shí)間: 2025-3-23 12:22 作者: scotoma 時(shí)間: 2025-3-23 16:11
,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作者: companion 時(shí)間: 2025-3-23 20:51 作者: 規(guī)范要多 時(shí)間: 2025-3-24 00:49
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作者: Indicative 時(shí)間: 2025-3-24 05:12 作者: Mercantile 時(shí)間: 2025-3-24 06:44 作者: Kaleidoscope 時(shí)間: 2025-3-24 14:32
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.作者: NEG 時(shí)間: 2025-3-24 17:19
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作者: CONE 時(shí)間: 2025-3-24 21:37 作者: flaggy 時(shí)間: 2025-3-25 03:04 作者: 有其法作用 時(shí)間: 2025-3-25 03:28 作者: 粗語(yǔ) 時(shí)間: 2025-3-25 10:20 作者: 索賠 時(shí)間: 2025-3-25 15:00
Machine Learning-Assisted Parameter Retrieval from Polarized Neutron Reflectometry Measurements,ity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb).Te./Cr.O. and identify possible interfacial proximity magnetism in this material. We anticipate t作者: 6Applepolish 時(shí)間: 2025-3-25 19:37 作者: 拾落穗 時(shí)間: 2025-3-25 20:06
Book 2022vel machine learning architectures are proposed in this thesis work which leverage the case when the data is scarce and utilize the internal symmetry of the system to improve the training quality. The work sheds light on future pathways to apply machine learning to augment experiments..作者: 鴕鳥(niǎo) 時(shí)間: 2025-3-26 02:11 作者: 分離 時(shí)間: 2025-3-26 07:28 作者: LOPE 時(shí)間: 2025-3-26 09:43
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