Accomplishment and challenge of materials database toward big data

The history and current status of materials data activities from handbook to database are reviewed, with introduction to some important products. Through an example of prediction of interfacial thermal resistance based on data and data science methods, we show the advantages and potential of material informatics to study material issues which are too complicated or time consuming for conventional theoretical and experimental methods. Materials big data is the fundamental of material informatics. The challenges and strategy to construct materials big data are discussed, and some solutions are proposed as the results of our experiences to construct National Institute for Materials Science（NIMS） materials databases.

Exploring the mathematic equations behind the materials science data using interpretable symbolic regression

Symbolic regression(SR),exploring mathematical expressions from a given data set to construct an interpretable model,emerges as a powerful computational technique with the potential to transform the“black box”machining learning methods into physical and chemistry interpretable expressions in material science research.In this review,the current advancements in SR are investigated,focusing on the underlying theories,fundamental flowcharts,various techniques,implemented codes,and application fields.More predominantly,the challenging issues and future opportunities in SR that should be overcome to unlock the full potential of SR in material design and research,including graphics processing unit accelera-tion and transfer learning algorithms,the trade-off between expression accuracy and complexity,physical or chemistry interpretable SR with generative large language models,and multimodal SR methods,are discussed.

Machine learning in materials genome initiative:A review

Discovering new materials with excellent performance is a hot issue in the materials genome initiative.Traditional experiments and calculations often waste large amounts of time and money and are also limited by various conditions. Therefore, it is imperative to develop a new method to accelerate the discovery and design of new materials. In recent years, material discovery and design methods using machine learning have attracted much attention from material experts and have made some progress. This review first outlines available materials database and material data analytics tools and then elaborates on the machine learning algorithms used in materials science. Next, the field of application of machine learning in materials science is summarized, focusing on the aspects of structure determination, performance prediction, fingerprint prediction, and new material discovery. Finally, the review points out the problems of data and machine learning in materials science and points to future research. Using machine learning algorithms, the authors hope to achieve amazing results in material discovery and design.

非线性光学数据库:理论预测助力材料的快速发现

现代激光技术迫切需要能够通过二次谐波产生相干光的非线性光学材料.然而,只有很少一部分非中心对称晶体材料的非线性光学性质被实验或者理论研究,针对高性能非线性光学晶体材料的探索仍非常有限.本工作建立了迄今为止最大的计算非线性光学晶体材料数据库,其中包含2354个非中心对称晶体结构,以及它们的倍频系数和带隙.本文提供了理论数据的计算细节和其与实验数据的对比验证,以及数据库的详细描述.该数据库的重要特点之一是它包含大量通过进化算法搜索发现的新的热力学稳定和亚稳定结构,这为寻找具有更好性能的新型非线性光学材料提供了可能性.

Applications of Machine Learning in Electrochemistry

The introduction of density functional theory(DFT)and electronic structure has brought computational methods into the field of materials science.In these theoretical calculations,quantum mechanics is predominantly used.Machine learning(ML)and high-throughput computing share some inherent similarities,as both can extract valuable information from massive datasets and possess parallelism and scalability.ML techniques simulate human thought processes,with algorithms that make decisions and have good scalability and strong generalization abilities.The combination of high-throughput and ML technologies leverages the advantages of high-throughput technology standardization and high capacity,addressing the challenges faced by ML at the front end.This complementary combination is expected to further enhance the efficiency of material screening and development.In data mining,using ML methods on various databases,the interrelationships between molecular structures and properties are discovered from large amounts of data.Mapping,current utilization of DFT,materials genomics,and high-throughput computing have generated a substantial amount of data.This review provides new insights into the development of electrochemistry.