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Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

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npj Computational Materials 2019年第1期5卷 966-982页

作者： Turab Lookman Prasanna V.Balachandran dezhen xue Ruihao Yuan Theoretical Division Los Alamos National LaboratoryLos AlamosNM 87545USA Department of Materials Science and Engineering University of VirginiaCharlottesvilleVA 22902USA State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University710049 Xi’anChina

One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are *** review how methods from the information sciences enable us to accelerate the search and discovery of new *** particular,active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and *** approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored *** discuss several utility functions and demonstrate their use in materials science applications,impacting both experimental and computational *** summarize by indicating generalizations to multiple properties and multifidelity data,and identify challenges,future directions and opportunities in the emerging field of materials informatics.

关键词： materials. Active directions

A property-oriented design strategy for high performance copper alloys via machine learning

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npj Computational Materials 2019年第1期5卷 363-370页

作者： Changsheng Wang Huadong Fu Lei Jiang dezhen xue Jianxin Xie Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China

Traditional strategies for designing new materials with targeted property including methods such as trial and error,and experiences of domain experts,are time and cost *** the present study,we propose a machine learning design system involving three features of machine learning modeling,compositional design and property prediction,which can accelerate the discovery of new *** demonstrate better efficiency of on a rapid compositional design of high-performance copper alloys with a targeted ultimate tensile strength of 600–950 MPa and an electrical conductivity of 50.0%international annealed copper *** exists a good consistency between the predicted and measured values for three alloys from literatures and two newly made alloys with designed *** results provide a new recipe to realize the property-oriented compositional design for highperformance complex alloys via machine learning.

关键词： alloys property copper

Temperature-field history dependence of the elastocaloric effect for a strain glass alloy

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Journal of Materials Science & Technology 2022年第8期103卷 8-14页

作者： Deqing xue Ruihao Yuan Yuanchao Yang Jianbo Pang Yumei Zhou Xiangdong Ding Turab Lookman Xiaobing Ren Jun Sun dezhen xue State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China School of Materials Science and Engineering Xi’an University of TechnologyXi’an 710048China AiMaterials Research Santa FeNew Mexico 87501USA Ferroic Physics Group National Institute for Materials ScienceTsukuba 305-0047IbarakiJapan

The singular change of the order parameter at the first order martensitic transformation(MT)temperature restricts the caloric response to a narrow temperature *** the MT is tuned into a sluggish strain glass transition by defect doping and a large elastocaloric effect appears in a wide temperature ***,an inverse elastocaloric effect is observed in the strain glass alloy with history of zerofield cooling and is attributed to the slow dynamics of the nanodomains in response to the external *** study offers a design recipe to expand the temperature range for good elastocaloric effect.

关键词： Elastocaloric effect Isothermal entropy change Strain glass

Improved stability of superelasticity and elastocaloric effect in Ti-Ni alloys by suppressing Lüders-like deformation under tensile load

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Journal of Materials Science & Technology 2023年第15期146卷 154-167页

作者： Pengfei Dang Jianbo Pang Yumei Zhou Lei Ding Lei Zhang Xiangdong Ding Turab Lookman Jun Sun dezhen xue State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China AiMaterials Research LLC Santa FeNM 87501USA

Functional stability of superelasticity is crucial for practical applications of shape memory *** is degraded by a Lüders-like deformation with elevated local stress concentration under tensile *** increasing the degree of solute supersaturation and applying appropriate thermomechanical treatments,a Ti-Ni alloy with nanocrystallinity and dispersed nanoprecipitates is *** contrast to conventional Ti-Ni alloys,the superelasticity in the target alloy is accompanied by homogeneous deformation due to the sluggish stress-induced martensitic *** alloy thus shows a fully recoverable strain of 6%under tensile stress over 1 GPa and a large adiabatic temperature decrease of 13.1 K under tensile strain of 4.5%at room ***,both superelasticity and elastocaloric effect exhibit negligible degradation in response to applied strain of 4%during *** attribute the improved functional stability to low dislocation activity resulting from the suppression of localized deformation and the combined strengthening effect of nanocrystalline structure and ***,the design of such a microstructure enabling homogeneous deformation provides a recipe for stable superelasticity and elastocaloric effect.

关键词： Ti-Ni alloys Superelasticity Elastocaloric effect Martensite band Functional stability

Machine learning assisted prediction of dielectric temperature spectrum of ferroelectrics

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Journal of Advanced Ceramics 2023年第9期12卷 1793-1804页

作者： Jingjin He Changxin Wang Junjie Li Chuanbao Liu dezhen xue Jiangli Cao Yanjing Su Lijie Qiao Turab Lookman Yang Bai Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology BeijingBeijing 100083China Institute for Advanced Material and Technology University of Science and Technology BeijingBeijing 100083China Faculty of Materials Science and Engineering Kunming University of Science and TechnologyKunming 650093China Sichuan Province Key Laboratory of Information Materials and Devices Application College of Optoelectronic EngineeringChengdu University of Information TechnologyChengdu 610225China School of Materials Science and Technology University of Science and Technology BeijingBeijing 100083China State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China AiMaterials Research LLC Santa FeNM 87501USA

In material science and engineering,obtaining a spectrum from a measurement is often time-consuming and its accurate prediction using data mining can also be *** this work,we propose a machine learning strategy based on a deep neural network model to accurately predict the dielectric temperature spectrum for a typical multi-component ferroelectric system,i.e.,(Ba_(1−x−y)Ca_(x)Sr_(y))(Ti_(1−u−v−w)Zr_(u)Sn_(v)Hf_(w))O_(3).The deep neural network model uses physical features as inputs and directly outputs the full spectrum,in addition to yielding the octahedral factor,Matyonov–Batsanov electronegativity,ratio of valence electron to nuclear charge,and core electron distance(Schubert)as four key *** to the physically meaningful features,our model exhibits better performance and generalization ability in the broader composition space of BaTiO3-based solid *** the prediction accuracy is superior to traditional machine learning models that predict dielectric permittivity values at each ***,the transition temperature and the degree of dispersion of the ferroelectric phase transition are easily extracted from the predicted spectra to provide richer physical *** prediction is also experimentally validated by typical samples of(Ba_(0.85)Ca_(0.15))(Ti_(0.98–x)Zr_(x)Hf_(0.02))O_(3).This work provides insights for accelerating spectra predictions and extracting ferroelectric phase transition information.

关键词： machine learning(ML) dielectric temperature spectrum ferroelectrics phase transition information

Machine learning assisted design of γ′-strengthened Co-base superalloys with multi-performance optimization

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npj Computational Materials 2020年第1期6卷 1138-1146页

作者： Pei Liu Haiyou Huang Stoichko Antonov Cheng Wen dezhen xue Houwen Chen Longfei Li Qiang Feng Toshihiro Omori Yanjing Su Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing100083 BeijingChina Corrosion and Protection Center University of Science and Technology Beijing100083 BeijingChina State Key Laboratory of Advanced Metals and Materials University of Science and Technology Beijing100083 BeijingChina State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong University710049 Xi’anChina College of Materials Science and Engineering Chongqing University400044 ChongqingChina Department of Materials Science Graduate School of EngineeringTohoku UniversitySendai 980-8579Japan

Designing a material with multiple desired properties is a great challenge,especially in a complex material ***,we propose a material design strategy to simultaneously optimize multiple targeted properties of multi-component Co-base superalloys via machine *** microstructural stability,γ′solvus temperature,γ′volume fraction,density,processing window,freezing range,and oxidation resistance were simultaneously optimized.

关键词： base optimization strengthened

Multi-objective optimization and its application in materials science

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Materials Genome Engineering Advances 2023年第2期1卷 60-76页

作者： Bofeng Shi Turab Lookman dezhen xue State Key Laboratory for Mechanical Behavior of Materials Xi'an Jiaotong UniversityXi'anChina AiMaterials Research LLC Santa FeNew MexicoUSA Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijingChina

Optimizing more than one property is inevitable in designing new materials;however,some properties are usually improved at the expense of *** optimization methods in engineering and computer science have proven to be an effective means to optimize several different properties ***,we reviewed these approaches including scalarization,evolutionary algorithms,and especially Bayesian *** promising applications to a number of materials problems are also discussed in the paper.

关键词： Bayesian optimization evolutionary algorithms materials design multi-objective optimization Pareto methods

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论马克思的“物我一体”哲学

马克思主义与现实 2009年第6期61卷 156-162页

作者：薛德震人民出版社

在《1844年经济学哲学手稿》中,马克思深刻揭示了人与自然界的关系,在《关于费尔巴哈的提纲》中,马克思又对旧唯物主义进行了深刻批判,从而为我们开启了"物我一体"新哲学的广阔思路。"物我一体"哲学并不是要泯灭物质和精神的区别,也不... 详细信息

在《1844年经济学哲学手稿》中,马克思深刻揭示了人与自然界的关系,在《关于费尔巴哈的提纲》中,马克思又对旧唯物主义进行了深刻批判,从而为我们开启了"物我一体"新哲学的广阔思路。"物我一体"哲学并不是要泯灭物质和精神的区别,也不是要撤销主体客体二分的界限,而是在承认这种区分的前提下强调它们之间的内在同一性,主张人与自然的和谐相处、和谐发展。以人为本哲学是马克思主义"物我一体"新哲学的当代形态。

关键词：马克思物我一体《1844年经济学哲学手稿》

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劳动异化论是唯心史观吗

理论前沿 2005年第5期 31-33页

作者：薛德震人民出版社

针对有学者发表文章认为马克思的劳动异化理论是唯心史观,本文提出了不同见解。

关键词：劳动异化论唯物史观唯心史观