Materials characterization remains a significant, time-consuming undertaking. Generally speaking, spectroscopic techniques are used in conjunction with empirical and ab-initio calculations in order to elucidate structure. These experimental and computational methods typically require significant human input and interpretation, particularly with regards to novel materials. Recently, the application of data mining and machine learning to problems in material science have shown great promise in reducing this overhead. In the work presented here, several aspects of machine learning are explored with regards to characterizing a model material, titania, using solid-state Nuclear Magnetic Resonance (NMR). Specifically, a large dataset is generated, corresponding to NMR $^{47}$Ti spectra, using ab-initio calculations for generated TiO$_2$ structures. Principal Components Analysis (PCA) reveals that input spectra may be compressed by more than 90%, before being used for subsequent machine learning. Two key methods are used to learn the complex mapping between structural details and input NMR spectra, demonstrating excellent accuracy when presented with test sample spectra. This work compares Support Vector Regression (SVR) and Artificial Neural Networks (ANNs), as one step towards the construction of an expert system for solid state materials characterization.
Native implementation of the machine-learned Skala exchange-correlation functional in CP2K: Unified one-centre reconstruction for molecular and condensed-phase calculations
Center for Advanced Systems Understanding (CASUS)（高级系统理解中心）；Helmholtz-Zentrum Dresden-Rossendorf（德累斯顿-罗斯多夫亥姆霍兹中心）；Department of Chemistry, University of Zurich（苏黎世大学化学系）；Institute of Artificial Intelligence, Technische Universität Dresden（德累斯顿工业大学人工智能研究所）
Machine learning clustering technique applied to powder X-ray diffraction patterns to distinguish alloy substitutions
为了研究磁性合金主相中取代基的微观结构，我们将DTW（dynamic time wraping）聚类技术应用于X射线衍射（XRD）谱图。聚类被发现执行良好，以确定成功率（约90%）的取代基的浓度。足够的性能归因于DTW处理的性质，以过滤掉不相关的信息，如峰强度（由于多晶样品中衍射条件的不可控性）和峰位置的均匀移位（由于晶格的热膨胀）。
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures
Graz University of Technology（格拉茨工业大学）；Know-Center GmbH, Research Center for Data-Driven Business & Big Data Analytics（知识中心有限公司（数据驱动业务与大数据分析研究中心））；Università di Roma La Sapienza（罗马第一大学）
