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Multi-scale investigation of short-range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

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npj Computational Materials 2023年第1期9卷 1430-1442页

作者： Hui Zheng Lauren T.W.Fey Xiang-Guo Li Yong-Jie Hu Liang Qi Chi Chen Shuozhi Xu Irene J.Beyerlein shyue ping ong Department of NanoEngineering University of California San DiegoLa JollaCA 92093-0448USA Materials Department University of CaliforniaSanta BarbaraCA 93106-5050USA Department of Materials Science and Engineering University of MichiganAnn ArborMI 48109USA Department of Materials Science and Engineering Drexel UniversityPhiladelphiaPA 19104USA Department of Mechanical Engineering University of CaliforniaSanta BarbaraCA 93106-5070USA

Refractory multi-principal element alloys(RMPEAs)are promising materials for high-temperature structural ***,we investigate the role of short-range ordering(SRO)on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling *** carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies(USFEs).From mesoscale phase-field dislocation dynamics simulations,we find that increasing SRO leads to higher mean USFEs and stress required for dislocation *** gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations,with higher SRO decreasing the degree of USFE dispersion and hence,amount of ***,we show how the morphology of an expanding dislocation loop is affected by the applied stress.

关键词： alloys dislocation element

A flexible and scalable scheme for mixing computed formation energies from different levels of theory

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npj Computational Materials 2022年第1期8卷 1857-1867页

作者： Ryan S.Kingsbury Andrew S.Rosen Ayush S.Gupta Jason M.Munro shyue ping ong Anubhav Jain Shyam Dwaraknath Matthew K.Horton Kristin A.Persson Department of Materials Science and Engineering University of California BerkeleyBerkeleyCA94720USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Materials Science Division Lawrence Berkeley National LaboratoryBerkeleyCA94720USA Miller Institute for Basic Research in Science University of CaliforniaBerkeleyCA94720USA University of California San Diego La JollaCA92093USA Molecular Foundry Lawrence Berkeley National LaboratoryBerkeleyCA94720USA

Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory(DFT),which now contain millions of calculations at the generalized gradient approximation(GGA)level of *** is now feasible to carry out high-throughput calculations using more accurate methods,such as meta-GGA DFT;however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing(GGA)***,we propose here a general procedure by which higher-fidelity,low-coverage calculations(e.g.,meta-GGA calculations for selected chemical systems)can be combined with lower-fidelity,high-coverage calculations(e.g.,an existing database of GGA calculations)in a robust and scalable *** then use legacy PBE(+U)GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions,and discuss practical considerations for its implementation.

关键词： theory scheme theory

An electrostatic spectral neighbor analysis potential for lithium nitride

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npj Computational Materials 2019年第1期5卷 508-515页

作者： Zhi Deng Chi Chen Xiang-Guo Li shyue ping ong Department of NanoEngineering University of California San Diego9500 Gilman DrMail Code 0448La JollaCA 92093-0448USA

Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction ***,a challenge in their application to ionic systems is the treatment of long-ranged ***,we present a highly accurate electrostatic Spectral Neighbor Analysis Potential(eSNAP)for ionicα-Li3N,a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion *** show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces,as well as various properties,such as lattice constants,elastic constants,and phonon dispersion *** also demonstrate the application of eSNAP in long-time,large-scale Li diffusion studies in Li3N,providing atomistic insights into measures of concerted ionic motion(e.g.,the Haven ratio)and grain boundary *** work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism,enabling large-scale atomistic simulations for such systems.

关键词： electrostatic lithium ionic

Complex strengthening mechanisms in the NbMoTaW multiprincipal element alloy

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npj Computational Materials 2020年第1期6卷 1074-1083页

作者： Xiang-Guo Li Chi Chen Hui Zheng Yunxing Zuo shyue ping ong Department of NanoEngineering University of California San Diego9500 Gilman DriveMail Code 0448La JollaCA 92093-0448USA

Refractory multi-principal element alloys(MPEAs)have exceptional mechanical properties,including high strength-to-weight ratio and fracture toughness,at high *** we elucidate the complex interplay between segregation,short-range order,and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic *** the single crystal MPEA,we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental *** the polycrystalline MPEA,we demonstrate that thermodynamically driven Nb segregation to the grain boundaries(GBs)and W enrichment within the grains intensifies the observed short-range order(SRO).The increased GB stability due to Nb enrichment reduces the von Mises strain,resulting in higher strength than a random solid solution *** results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs.

关键词： strengthening alloy toughness

Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain Francesca Tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm shyue ping ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction in *** contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL *** this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language *** each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and *** conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

Automated generation and ensemble-learned matching of X-ray absorption spectra

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npj Computational Materials 2018年第1期4卷 566-574页

作者： Chen Zheng Kiran Mathew Chi Chen Yiming Chen Hanmei Tang Alan Dozier Joshua J.Kas Fernando D.Vila John J.Rehr Louis F.J.Piper Kristin A.Persson shyue ping ong Department of NanoEngineering University of California San Diego9500 Gilman DriveMail Code 0448La JollaCA 92093-0448USA Department of Materials Science University of California BerkeleyBerkeleyCA 94720USA Division of Applied Research and Technology National Institute for Occupational Safety and HealthCenters for Disease ControlCincinnatiOH 45226USA Department of Physics University of WashingtonSeattleWA 98195USA Department of Physics Applied Physics and AstronomyBinghamton UniversityBinghamtonNY 13902USA Materials Science&Engineering Binghamton UniversityBinghamtonNY 13902USA

X-ray absorption spectroscopy(XAS)is a widely used materials characterization technique to determine oxidation states,coordination environment,and other local atomic structure *** of XAS relies on comparison of measured spectra to reliable reference ***,existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry *** this work,we report the development of XASdb,a large database of computed reference XAS,and an Ensemble-Learned Spectra IdEntification(ELSIE)algorithm for the matching of *** currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra(XANES)for over 40,000 materials from the open-science Materials Project *** discuss a high-throughput automation framework for FEFF calculations,built on robust,rigorously benchmarked *** is a computer program uses a real-space Green’s function approach to calculate X-ray absorption *** will demonstrate that the ELSIE algorithm,which combines 33 weak“learners”comprising a set of preprocessing steps and a similarity metric,can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal *** XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project,providing an important new public resource for the analysis of XAS to all materials ***,the ELSIE algorithm itself has been made available as part of veidt,an open source machine-learning library for materials science.

关键词： matching XANES ensemble

燕麦不同组合正、反交杂种后代的表型及遗传参数分析

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草业学报 2024年第4期33卷 73-86页

作者：慕平柴继宽苏玮娟章海龙赵桂琴甘肃农业大学农学院甘肃兰州730070 甘肃农业大学草业学院甘肃兰州730070 青海省三江集团有限责任公司青海西宁810003

为了明确燕麦不同组合正、反交杂种后代的表型差异及遗传变异情况,本研究以福瑞至(F709)与709(709F)、牧乐思(ML2)与陇燕2号(L2M)为亲本配制正、反交组合,用简单序列重复分子标记对杂种F1代进行了鉴定,比较了正、反交杂种的表型差异,并... 详细信息

为了明确燕麦不同组合正、反交杂种后代的表型差异及遗传变异情况,本研究以福瑞至(F709)与709(709F)、牧乐思(ML2)与陇燕2号(L2M)为亲本配制正、反交组合,用简单序列重复分子标记对杂种F1代进行了鉴定,比较了正、反交杂种的表型差异,并分析了杂种F_(2)代的遗传变异及多样性。结果表明:用SSR分子标记从37个F_(1)单株中共鉴定出32个真杂种,占比86.5%。福瑞至和709、牧乐思和陇燕2号组合的F_(1)真杂种率分别为88.9%和84.2%。正、反交对杂种后代的表型有显著影响。F709-F_(1)的旗叶比反交的窄19.4%;ML2-F_(1)的主穗粒数比反交少12.2%。F_(2)群体遗传变异幅度较大。同一性状在正、反交F_(2)中的遗传变异系数(G_(cv))、广义遗传力(Hb)和相对遗传进度(ΔGt)存在显著差异。F_(2)群体株高的遗传变异系数较小(4.50%~10.27%)、广义遗传力较高(33.12%~85.41%),分蘖数和有效分蘖数的相对遗传进度较大。福瑞至和709的正、反交F2群体的穗下节长的广义遗传力是所有指标中最低的,分别为14.13%和23.81%,相对遗传进度也最小。主穗粒数的Gcv在其正、反交F_(2)中的差异最显著,在F709-F_(2)中为7.09%,反交中增至19.03%;F709-F_(2)的Hb为22.54%,反交则为66.01%。牧乐思和陇燕2号的正、反交F_(2)群体叶片数的广义遗传力最高(99.53%),几乎不受环境的影响。ML2-F_(2)旗叶宽的Hb为9.07%,其ΔG_(t)为2.79,反交中旗叶宽的Hb为30.23%,ΔGt为9.77。10对SSR引物对F2群体扩增的多态性比率达73.5%,实际杂合度为0.35~0.99,远高于期望杂合度;Nei’s基因多态性、香农指数分别为0.29~0.50和0.53~0.71,F_(2)群体分离现象明显,遗传变异幅度大,具有较大的选择空间。

关键词：燕麦杂种鉴定表型性状遗传变异

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微起爆序列用硅基作动机构隔断特性研究

火工品 2024年第3期 1-6页

作者：胡腾江赵玉龙谭明何亚新刘运斌屈萍西安交通大学精密微纳制造技术全国重点实验室陕西西安710049 四川华川工业股份有限公司技术中心四川成都610100 陕西应用物理化学研究所陕西西安710061

针对MEMS火工品的内置安全控制设计需求,在硅基MEMS作动机构的基础上,重点开展了硅基作动机构与微起爆传爆序列的集成化设计与能量匹配研究。该序列主要由微起爆器、微作动机构及传爆药组成。其中微起爆器的叠氮化铜装药尺寸为Ф0.8mm×... 详细信息

针对MEMS火工品的内置安全控制设计需求,在硅基MEMS作动机构的基础上,重点开展了硅基作动机构与微起爆传爆序列的集成化设计与能量匹配研究。该序列主要由微起爆器、微作动机构及传爆药组成。其中微起爆器的叠氮化铜装药尺寸为Ф0.8mm×0.8mm,装药量为1.5mg;微作动机构在11V直流电压驱动下,可以实现传爆通道的开启与闭合,隔断位移为743.06µm;传爆药为油墨直写方式制备的CL-20传爆药,装药尺寸为Ф3mm×3mm,装药密度为1.66g/cm^(3),装药量约为35mg。研究表明该序列具备可靠起爆、传爆以及隔爆功能。

关键词：微起爆传爆序列微起爆器微作动机构传爆药

分子伴侣增强蛋白酶K在毕赤酵母中的表达及对羊毛鳞片层的作用分析

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生物技术通报 2024年第7期40卷 307-313页

作者：蔡逸安张轶群杨子璇刘业学刘文龙路福平李玉天津科技大学生物工程学院天津300457 山东隆科特酶制剂有限公司沂水276400

【目的】通过分子伴侣共表达增强毕赤酵母中异源蛋白酶K的分泌水平,并对其的羊毛无氯剥鳞作用机制进行解析,旨在提高蛋白酶K的表达量并为高效酶法剥鳞技术的应用奠定基础。【方法】利用毕赤酵母表达系统对tprK基因进行异源表达,首次分... 详细信息

【目的】通过分子伴侣共表达增强毕赤酵母中异源蛋白酶K的分泌水平,并对其的羊毛无氯剥鳞作用机制进行解析,旨在提高蛋白酶K的表达量并为高效酶法剥鳞技术的应用奠定基础。【方法】利用毕赤酵母表达系统对tprK基因进行异源表达,首次分析了影响蛋白质折叠和质量控制的分子伴侣Ssa1、Erj5、Sil1、Hac1、Kar2、Lhs1和Ydj1分别过表达对TPRK的表达量和酶活力的作用,并对TPRK处理的羊毛纤维效果进行分析。【结果】TPRK在毕赤酵母GS115中表达,其最适反应条件为65℃、pH 9.0,且具有良好的热稳定性和pH稳定性。过表达ssa1、hac1、erj5和sil1基因的重组菌株酶活分别提升了36.8%、20.0%、17.7%和14.8%。5 L发酵罐进行高密度发酵,诱导72 h后,TPRK的酶活达到77471.99 U/mL。在羊毛水解应用中TPRK可水解羊毛鳞片内层使鳞片逐渐剥落,起到剥鳞效果,并且最适水解条件为:TPRK添加量为300 U/mL、反应温度55℃、pH 9.0和反应时间2 h。【结论】过表达分子伴侣Ssa1能够有效提升TPRK的表达量,利用该TPRK处理羊毛纤维,可有效去除羊毛鳞片层,而对羊毛核心皮质层造成的损伤较小。

关键词：蛋白酶K 分子伴侣毕赤酵母生物酶处理防毡缩