Advances of Machine Learning in Materials Science: Ideas and Techniques

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.Comment: 80 pages; 22 figures. To be published in Frontiers of Physics, 18, xxxxx, (2023

Nanoparticle manipulation by thermal gradient

Specialized Research Fund for the Doctoral Program of Higher Education [20090121120028, 20100121120026]; Natural Science Foundation of Fujian Province, China [2010J05138]; Program for New Century Excellent Talents in University (NCET) [NCET-09-0680]A method was proposed to manipulate nanoparticles through a thermal gradient. The motion of a fullerene molecule enclosed inside a (10, 10) carbon nanotube with a thermal gradient was studied by molecular dynamics simulations. We created a one-dimensional potential valley by imposing a symmetrical thermal gradient inside the nanotube. When the temperature gradient was large enough, the fullerene sank into the valley and became trapped. The escaping velocities of the fullerene were evaluated based on the relationship between thermal gradient and thermophoretic force. We then introduced a new way to manipulate the position of nanoparticles by translating the position of thermostats with desirable thermal gradients. Compared to nanomanipulation using a scanning tunneling microscope or an atomic force microscope, our method for nanomanipulation has a great advantage by not requiring a direct contact between the probe and the object

Tailoring of polar and nonpolar ZnO planes on MgO (001) substrates through molecular beam epitaxy

Polar and nonpolar ZnO thin films were deposited on MgO (001) substrates under different deposition parameters using oxygen plasma-assisted molecular beam epitaxy (MBE). The orientations of ZnO thin films were investigated by in situ reflection high-energy electron diffraction and ex situ X-ray diffraction (XRD). The film roughness measured by atomic force microscopy evolved as a function of substrate temperature and was correlated with the grain sizes determined by XRD. Synchrotron-based X-ray absorption spectroscopy (XAS) was performed to study the conduction band structures of the ZnO films. The fine structures of the XAS spectra, which were consistent with the results of density functional theory calculation, indicated that the polar and nonpolar ZnO films had different electronic structures. Our work suggests that it is possible to vary ZnO film structures from polar to nonpolar using the MBE growth technique and hence tailoring the electronic structures of the ZnO films

Breast cancer stage at diagnosis and area-based socioeconomic status: a multicenter 10-year retrospective clinical epidemiological study in China

<p>Abstract</p> <p>Background</p> <p>Although socioeconomic status (SES) has been focused on as a key determinant of cancer stage at diagosis in western countries, there has been no systemic study on the relationship of SES and breast cancer stage at diagnosis in China.</p> <p>Methods</p> <p>The medical charts of 4,211 eligible breast cancer patients from 7 areas across China who were diagnosed between 1999 and 2008 were reviewed. Four area-based socioeconomic indicators were used to calculate area-based SES by cluster analysis. The associations between area-based SES and stage at diagnosis were analyzed by trend chi-square tests. Binary logistic regression was performed to estimate odds ratios for individual demographic characteristics' effects on cancer stages, stratified by area-based SES.</p> <p>Results</p> <p>The individual demographic and pathologic characteristics of breast cancer cases were significantly different among the seven areas studied. More breast cancer cases in low SES areas (25.5%) were diagnosed later (stages III & IV) than those in high (20.4%) or highest (14.8%) SES areas (<it>χ</it>²for trend = 80.79, <it>P </it>< 0.001). When area-based SES is controlled for, in high SES areas, cases with less education were more likely to be diagnosed at later stages compared with more educated cases. In low SES areas, working women appeared to be diagnosed at earlier breast cancer stages than were homemakers (OR: 0.18-0.26).</p> <p>Conclusions</p> <p>In China, women in low SES areas are more likely to be diagnosed at later breast cancer stages than those in high SES areas.</p