Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

MatChat: A large language model and application service platform for materials science

The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.

Molecular Dynamics (MD) Applications in Materials Science and Engineering and Nanotechnology

Molecular dynamics (MD) is a computer simulation technique that helps to explore the behavior and properties of molecules and atoms. MD has been used in research and development in many spaces, including materials science and engineering and nanotechnology. MD has been proven useful in topics like the nano-engineering of construction materials, correcting graphene planar defects, studying self-assembling bio-materials, and the densification, consolidation, and sintering of nanocrystalline materials.

Development of advanced electron tomography in materials science based on TEM and STEM

The recent developments of electron tomography（ET） based on transmission electron microscopy（TEM） and scanning transmission electron microscopy（STEM） in the field of materials science were introduced. The various types of ET based on TEM as well as STEM were described in detail, which included bright-field（BF）-TEM tomography, dark-field（DF）-TEM tomography, weak-beam dark-field（WBDF）-TEM tomography, annular dark-field（ADF）-TEM tomography, energy-filtered transmission electron microscopy（EFTEM） tomography, high-angle annular dark-field（HAADF）-STEM tomography, ADF-STEM tomography, incoherent bright field（IBF）-STEM tomography, electron energy loss spectroscopy（EELS）-STEM tomography and X-ray energy dispersive spectrometry（XEDS）-STEM tomography, and so on. The optimized tilt series such as dual-axis tilt tomography, on-axis tilt tomography, conical tilt tomography and equally-sloped tomography（EST） were reported. The advanced reconstruction algorithms, such as discrete algebraic reconstruction technique（DART）, compressed sensing（CS） algorithm and EST were overviewed. At last, the development tendency of ET in materials science was presented.

Progress of Materials Science in Space Technology in China(2020-2022)

In this paper,the main research work and related reports of materials science research in China’s space technology field during 2020-2022 are summarized.This paper covers Materials Sciences in Space Environment,Materials Sciences for Space Environment,Materials Behavior in Space Environment and Space experimental hardware for material investigation.With the rapid development of China’s space industry,more scientists will be involved in materials science,space technology and earth science researches.In the future,a series of disciplines such as space science,machinery,artificial intelligence,digital twin and big data will be further integrated with materials science,and space materials will also usher in new development opportunities.

Space Materials Science in China:Ⅱ.Ground-based Researches and Academic Activities

Activities of space materials science research in China have been continuously supported by two main national programs.One is the China Space Station(CSS)program since 1992,and the other is the Strategic Priority Program(SPP)on Space Science since 2011.In CSS plan in 2019,eleven space materials science experimental projects were officially approved for execution during the construction of the space station.In the SPP Phase Ⅱ launched in 2018,seven pre-research projects are deployed as the first batch in 2018,and one concept study project in 2019.These pre-research projects will be cultivated as candidates for future selection as space experiment projects on the recovery of scientific experimental satellites in the future.A new apparatus of electrostatic levitation system for ground-based research of space materials science and rapid solidification research has been developed under the support of the National Natural Science Foundation of China.In order to promote domestic academic activities and to enhance the advancement of space materials science in China,the Space Materials Science and Technology Division belong to the Chinese Materials Research Society was established in 2019.We also organized scientists to write five review papers on space materials science as a special topic published in the journal Scientia Sinica to provide valuable scientific and technical references for Chinese researchers.

Institute of Materials Science at Jilin University——A Brief Overview

The Institute of Materials Science atJilin University(IMSJU),located inChangchun,north east of China,is a majorresearch center in Jilin University which isone of the key universities in China.Its mainactivity is research and development in thefield of materials science and technology,especially concerning the advanced

Creative Economy and Materials Science & Engineering

New materials are fundamental to the growth,security,and quality of life of human being sand open doors to technologies in civil,chemical,nuclear,aeronautical,mechanical,biomedical,and electrical engineering.Creative companies use multiple materials in the development of their activities,such as solid stone,fiber glass,concrete,and glass reinforced concrete,for example.Based on bibliographic research,the article examines the synergy between materials science&engineering and creative economy.The main argument indicates that this synergy creates solutions and functionalities that add value to existing products and allow the development of new products with competitive advantages.It may also contribute to the preservation of cultural values and promote sustainability.