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Chinese Physics B 2024年第5期33卷 117-123页

作者：吴思远朱天念涂思佳肖睿娟袁洁吴泉生李泓翁红明 Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical sciences University of Chinese Academy of SciencesBeijing 100190China College of materials science and Optoelectronic Technology University of Chinese Academy of SciencesBeijing 100049China Condensed Matter Physics Data Center of Chinese Academy of sciences Beijing 100190China

The exponential growth of literature is constraining researchers’access to comprehensive information in related *** natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled *** 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 *** 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 *** 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 ***,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.

关键词： natural language processing text mining materials science

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Advances of machine learning in materials science: Ideas and techniques

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Frontiers of physics 2024年第1期19卷 155-194页

作者： Sue Sin Chong Yi Sheng Ng Hui-Qiong Wang Jin-Cheng Zheng Department of New Energy science and Engineering Xiamen University MalaysiaSepang 43900Malaysia Engineering Research Center of Micro-nano Optoelectronic materials and Devices Ministry of EducationFujian Key Laboratory of Semiconductor Materials and ApplicationsCI Center for OSEDand Department of PhysicsXiamen UniversityXiamen 361005China

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.

关键词： machine learning materials science

Data quantity governance for machine learning in materials science

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National science Review 2023年第7期10卷 234-250页

作者： Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi School of Computer Engineering and science Shanghai University Shanghai Engineering Research Center of Intelligent Computing System Australian Nuclear science and Technology Organisation School of Chemistry The University of Sydney State Key Laboratory of Advanced Special Steel School of Materials Science and Engineering Shanghai University materials Genome Institute Shanghai University

Data-driven machine learning(ML) is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate ***,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models) or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible ***,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity *** this,we propose a synergistic data quantity governance flow with the incorporation of materials domain *** summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and *** work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

关键词： machine learning data governance data quantity materials science

Generative artificial intelligence and its applications in materials science:Current situation and future perspectives

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Journal of Materiomics 2023年第4期9卷 798-816页

作者： Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi School of Computer Engineering and science Shanghai UniversityShanghai200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai200444China materials Genome Institute Shanghai UniversityShanghai200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai200444China Australian Nuclear science and Technology Organisation Sydney2232Australia School of Chemistry The University of SydneySydney2006Australia

Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required *** the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity ***,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of *** applications of task-specific generative models involving materials inverse design and data augmentation are also *** ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials ***,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding *** work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development.

关键词： Machine learning Artificial intelligence Generative artificial intelligence materials science Novel materials discovery Deep learning

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

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Chinese Physics B 2023年第11期32卷 173-178页

作者：陈子逸谢帆恺万萌袁扬刘淼王宗国孟胜王彦棡 Computer Network Information Center Chinese Academy of SciencesBeijing 100083China University of Chinese Academy of sciences Beijing 100049China Institute of Physics Chinese Academy of SciencesBeijing 100190China School of Physical sciences University of Chinese Academy of SciencesBeijing 100190China Songshan Lake materials Laboratory Dongguan 523808China Center of materials science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

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 *** 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.

关键词： MatChat materials science generative artificial intelligence

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Aggregation-Induced Emission:A Rising Star in Chemistry and materials science

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Chinese Journal of Chemistry 2021年第3期39卷 677-689页

作者： Ting Han Dingyuan Yan Qian Wu Nan Song Haoke Zhang Dong Wang Center for AIE Research Shenzhen Key Laboratory of Polymer Science and TechnologyGuangdong Research Center for Interfacial Engineering of Functional MaterialsCollege of Materials Science and EngineeringShenzhen UniversityShenzhenGuangdong 518060China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhenGuangdong 518060China Department of Chemistry The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Aggregation-induced emission(AIE)refers to a photophysical effect that the luminescence of aggregates is stronger than that of the dispersed *** the concept of AIE was coined by Professor Ben Zhong Tang and co-workers in 2001,AIE has evolved from a simple luminescent phenomenon to a multidisciplinary research field with a widespread *** has changed people's way of thinking about chromophore aggregation and greatly promoted the development of advanced luminescent *** the 20-year development,diverse AIE luminogens(AlEgens)with attractive functionalities have been developed and remarkable achievements have been made in the mechanistic study and high-tech applications of *** this review,we provide an overview of the historical development and representative achievements of AIE *** on the application of AIE in aggregate science are also briefly discussed to guide the future development in this field.

关键词： Aggregation-induced emission Fluorescence materials science Structures Mechanisms

Journal of　materials science & TECHNOLOGY　Volume 20 2004　CONTENTS

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Journal of materials science & Technology 2004年第6期 803-811页

Journal of materials science ＆ TECHNOLOGY

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Journal of materials science & Technology 2004年第6期20卷 i001-i009页

Metallurgy, the Father of materials science

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Tsinghua science and Technology 2002年第1期7卷 1-5页

作者： Robert W. CahnDepartment of Metallurgy and materials science, Cambridge University, Pembroke Street, Cambridge CB2 3QZ, UK Department of Metallurgy and materials science Cambridge University Pembroke Street Cambridge CB2 3QZ UK

The evolution of the discipline of materials science during the second half of the twentieth century is outlined. The concept emerged in the USA, almost simultaneously in an academic metallurgy department and in an avant garde industrial research laboratory, and its development subsequently all over the world has been a joint enterprise involving universities, industrial laboratories and government establishments. The initial impetus came unambiguously from the well established discipline of physical metallurgy, but from the 1960s onwards, the input from solid state physicists grew very rapidly, while materials chemistry is a later addition. Of all the many subdivisions of modern materials science, polymer science has been the slowest to fit under the umbrella of the broad discipline; its concepts are very different from those familiar to metallurgists. Two fields have contributed mightily to the creation of modern materials science: One is nuclear energy and, more specifically, the study of radiation damage, the other is the huge field of electronic and opto electronic materials in which physics, chemistry and metallurgy are seamlessly combined. 

关键词： materials science physical metallurgy disciplinary evolution polymer science electronic materials radiation damage

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FUTURE OF APPLIED STEREOLOGY IN materials science AND ENGINEERING

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FUTURE OF APPLIED STEREOLOGY IN MATERIALS SCIENCE AND ENGINE...