مقاله انگلیسی رایگان در مورد داده کاوی مواد انرژی جدید از ساختار بانکهای اطلاعاتی – الزویر 2019

1- Introduction

2- Structural databases

3- Data mining techniques

4- Examples of data mining energy materials from structure databases

5- Suggestions and outlook

6- Conclusions

References

Abstract

New energy materials that act as clean power sources and data science are developing rapidly in the past decades and the advancement of the two research areas have significantly benefited the development of each other. At the meantime, structural information of materials have been obtained and stored in various structure databases, such as the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Database (ICSD). Researchers have developed various structure-property relationships of the energy materials, which could be applied to screen the potential suitable materials from structure databases; this has become an efficient route to explore and design new energy materials. In this article, we review recent progresses on the data mining study of new energy materials based on structure databases such as CSD and ICSD, in the context of dye-sensitized solar cells and perovskite solar cells, and also include other energy systems such as water splitting systems, lithium batteries, thermoelectric devices and gas adsorbent materials. The structure descriptors that are more fundamental in the data mining procedure employing the structure-properties relationships are focused; the structural descriptors are complementary to the quantum descriptors and are efficient in the materials design process. We believe that with the successful formulation of more advanced and case-by-case structure-property relationships of energy materials, many new energy materials could be efficiently identified with much lower cost and shorter design period via the data mining process.

Introduction

There has been global energy crisis and environmental issues in the past decades due to the overuse of fossil fuels. New energy materials should be identified and developed to provide clean energy [1–5]. For example, efficient solar cell materials should be explored to harness the clean energy from the non-exhaustible solar system to provide the electricity; materials incorporated in water-splitting systems should be screened to provide solar fuels; lithium-based materials could be exploited to be tailored to undergo charge/discharge process reversibly and store electrical energies for portable devices and vehicles; metalorganic frameworks could be discovered to capture the carbon and gas molecules effectively; thermoelectric materials and piezoelectric materials should be identified to covert the thermal and mechanical energy into the electric current. At the current stage, the discovery of these energy materials relies predominantly on the experimental serendipity and the try-and-error experimental process that are inefficient and time-consuming. Nevertheless, many evidences have shown that the discovery process of new energy materials could be greatly accelerated by the data mining process, which have already shown their excellence in pharmaceutical drug discovery, finance, medicine, and marketing [6,7]. A large amount of structure information of new materials have been determined and stored in structure databases, which are mainly prepared by the crystallization processes and solved by the X-ray diffraction techniques and other crystallographic techniques [8]. Most of these materials have their structures accurately determined within the resolution of 0.01 Å and serve as outstanding platforms for the structural analysis and more advanced structure-property analysis. Two outstanding databases of materials structures have emerged: the Cambridge Structure Database (CSD) and the Inorganic Crystal Structure Database (ICSD), which focus on the accurate crystal structures of organic and inorganic materials, respectively [9,10]. These databases formed a good foundation for the data mining process of new energy materials. These databases store structures of materials that have already been synthesized in laboratory and exhibit significant advantages to expedite the process towards achieving promising candidates [11,12].