Machine learning-based approaches for designing high-performance materials; Machine learning-assisted simulations in materials science; Data-driven modeling of composition-processing-structure-property relationships; Machine learning-assisted techniques for characterizing the microstructure and structure of materials;...
The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational de
inverse design problem) have been scarce, with trial-and-error (based on time-consuming experimental and computational iterations) remaining the prevalent approach. The inverse problem in the realm of Kirigami metamaterials has primarily been tackled using optimization methods9,10...
Machine learning plays an important role in accelerating the discovery and design process for novel electrochemical energy storage materials. This review aims to provide the state-of-the-art and prospects of machine learning for the design of rechargeable battery materials. After illustrating the key ...
High-throughput and data-driven machine learning techniques for discovering high-entropy alloys directions and perspectives for the materials genome-assisted design of HEAs are proposed and discussed.#High-entropy alloys exhibit attractive property ... Z Lu,D Ma,X Liu,... - 《Communications Material...
Machine-learning-assisted materials discovery using failed experiments 基于失败实验的机器学习辅助材料发现.pdf,LETTER doi:10.1038/nature17439 Machine-learning-assisted materials discovery using failed experiments 1 1 1 1 1 1 2 Paul Raccuglia , Katherine C
machinelearningfailedmaterialsassistedusing 5MAY2016|VOL533|NATURE|73LETTERdoi:10.1038/nature17439Machine-learning-assistedmaterialsdiscoveryusingfailedexperimentsPaulRaccuglia1,KatherineC.Elbert1,PhilipD.F.Adler1,CaseyFalk1,MaliaB.Wenny1,AurelioMollo1,MatthiasZeller2,SorelleA.Friedler1,JoshuaSchrier1&Alexander...
Machine-learning-assisted materials discovery using failed experiments Nature, 533 (2016), pp. 73-76 CrossrefView in ScopusGoogle Scholar [29] L.-Q. Chen, L.-D. Chen, S.V. Kalinin, G. Klimeck, S.K. Kumar, J. Neugebauer, et al. Design and discovery of materials guided by theory an...
Chinese Academy of Sciences Reports Findings in Machine Learning (Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength) 来自 国家科技图书文献中心 喜欢 0 阅读量: 35 摘要: By a News Reporter-Staff News Editor at Robotics & Machine Learning...
Material design Among all of the properties of the γ′-strengthened Co-base superalloys, the γ′ solvus temperature is of the greatest importance, as it determines the upper temperature capability limit. Therefore, in order to develop advanced single crystal materials, a high γ′ solvus temperat...