It is safe to say that every invention that has changed the world has depended on materials. At present, the demand for the development of materials and the invention or design of new materials is becoming more and more urgent since peoples’ current production and lifestyle needs must be changed to help mitigate the climate. Structure-property relationships are a vital paradigm in materials science. However, these relationships are often nonlinear, and the pattern is likely to change with length scales and time scales, posing a huge challenge. With the development of physics, statistics, computer science, etc., machine learning offers the opportunity to systematically find new materials. Especially by inverse design based on machine learning, one can make use of the existing knowledge without attempting mathematical inversion of the relevant integrated differential equation of the electronic structure but by using backpropagation to overcome local minimax traps and perform a fast calculation of the gradient information for a target function concerning the design variable to find the optimizations. The methodologies have been applied to various materials including polymers, photonics, inorganic materials, porous materials, 2-D materials, etc. Different types of design problems require different approaches, for which many algorithms and optimization approaches have been demonstrated in different scenarios. In this mini-review, we will not specifically sum up machine learning methodologies, but will provide a more material perspective and summarize some cut-edging studies.

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The electron-energy-loss-spectrum of sodium has been measured in discrete region a t a 1.5 keV impact energy and a mean scattering angle of 0". The relative optical oscillator strength density spectrum of the valence shell of sodium was established by using the dipole (e, e) method and then normalized to the theoretical optical oscillator strength of the 32S + 32P excitation. The optical oscillator strengths of the 32S + 4'P and 3 ' s + 5'P excitations obtained in the present work are 0.014 k 0.002 and 0.0026 k 0.0004, respectively. Theoretical values of optical oscillator strengths for 32S + n2P (n = 3 -5 ) excitations of sodium were obtained by using the relativistic configuration interaction method, which are 0.97, 0.012, and 0.0025, respectively.

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In the development of HTTP protocol, the technique to overlap multiple HTTP requests and replies on single TCP connection, called 'keep-alive' or 'persistent connection', has won great success. It is already verified that, persistent connection could help to save the cost of frequently creating TCP connection, and could also reduce the number of operations such like forking and destroying process. Many years passed, persistent connection mechanism has been implemented widely to support all kinds of web services. However recently, dramatic changes to networking conditions and server computing capacity challenge the motivations of such mechanism and reveal some of its drawbacks. This paper models the connection management procedure in concurrent web servers with Petri Nets, and determines the parameters for the model by measuring a bunch of key metrics in modern web servers and networks. Plus, experiments are carried out in real modern test-bed with real traffic. The analytical results yielded by the model, along with experimental results, help us to clarify the negative role that persistent connection actually plays in modern web servers, especially in those busy ones.