|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Benchmarking materials property prediction methods:the Matbench test set and Automatminer reference algorithm显示文摘We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. | Alexander Dunn Qi Wang Alex Ganose Daniel Dopp Anubhav Jain | 2020 | npj Computational Materials2020，，1： | 7 |
| 2 | Author Correction:Benchmarking materials property prediction methods:the Matbench test set and Automatminer reference algorithm显示文摘The original version of the Article contained an error in Fig.3,in which the label at the top of the first column of Fig.3 originally incorrectly read‘Yield Strength(GPa)’,rather than the correct‘Yield Strength(MPa)’.This has been corrected in both the PDF and HTML versions of the Article. | Alexander Dunn Qi Wang Alex Ganose Daniel Dopp Anubhav Jain | 2020 | npj Computational Materials2020，，1： | 7 |
| 3 | A critical examination of compound stability predictions from machine-learned formation energies显示文摘Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory(DFT).The models tested in this work include five recently published compositional models,a baseline model using stoichiometry alone,and a structural model.By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions,we show that while formation energies can indeed be predicted well,all compositional models perform poorly on predicting the stability of compounds,making them considerably less useful than DFT for the discovery and design of new solids.Most critically,in sparse chemical spaces where few stoichiometries have stable compounds,only the structural model is capable of efficiently detecting which materials are stable.The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery,with the constraint that for any new composition,the ground-state structure is not known a priori.This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability,emphasizing the importance of assessing model performance on stability predictions,for which we provide a set of publicly available tests. | Christopher J.Bartel Amalie Trewartha Qi Wang Alexander Dunn Anubhav Jain Gerbrand Ceder | 2020 | npj Computational Materials2020，，1： | 7 |
| 4 | 对设备维修中人为失误的控制显示文摘大量研究证实,超过50%的设备在维修之后过早地失效了。更无奈的是,正是维修人员的维修行为加快了设备失效的速度。在近期学术研究和实践经验的基础上,笔者列出了维修管理人员应该注意的问题, | Alexander (Sandy) Dunn 李晶晶 | 2010 | 中国设备工程2010，，6： | 2 |
| 5 | Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials显示文摘Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy. | Maxwell TDylla Alexander Dunn Shashwat Anand Anubhav Jain G.Jeffrey Snyder | 2020 | Research2020，，1： | 2 |
| 6 | Design and development of a high performance passive mm-wave imager for aeronautical applications 显示文摘 | Lettington A Dunn D Alexander N | 2004 | SPIE Proc2004，5410，： | 1 |
| 7 | De- sign and development of a high-performance passive millimeter-wave imager for aeronautical applications显示文摘 | Lettington A H Dunn D Alexander N E D | 2004 | SPIE2004，44，9： | 1 |
| 8 | A New op- to-mechanical scanner for millimeter and sub-millime- ter wave imaging显示文摘 | Lettington A H Alexander N E Dunn D | 2005 | SPIE2005，5789，： | 1 |
| 9 | Antithrombotic therapy in atrial fibrillation显示文摘 | Dunn M Alexander J Clesilva R | 1989 | Chest1989，95，： | 1 |
| 10 | Design and development of a high performance passive ram-wave imagcr for aeronautieal applications 显示文摘 | Lettington A Dunn D Alexander N | 2004 | Proc of SPIE2004，5410，： | 1 |
| 11 | Design and development of a high performance passive mm-wave imager for aeronautical applications显示文摘 | Lettington A Dunn D Alexander N | 2004 | Proc of SPIE2004，5410，： | 1 |
| 12 | Electron tunneling through sensitizer wires bound to proteins显示文摘 | Matthew R. Hartings Igor V. Kurnikov Alexander R. Dunn Jay R. Winkler Harry B. Gray Mark A. Ratner | 2009 | Coordination Chemistry Reviews2009，，3： | 1 |
| 13 | Determination of the vapor pressure curves of adipic acid and triethanolamine using thermogravimetric analysis显示文摘 | WRIGHT S F DOLLIMOREL D DUNN J G ALEXANDER K | 2004 | Thermochimica Acta2004，421，： | 1 |
| 14 | Antithrombotic therapy in atrial fibrillation显示文摘 | Alexander J Clesilva R | 1989 | Chest1989，，： | 1 |
| 15 | Structural Differences Be-tween Similar Commercial Grades of Polyvinyl Alcohol - Acetate显示文摘 | Alexander S Dunn Shreeang R Naravane | 1980 | The British Polymer Journal1980，，7： | 1 |
| 16 | 企业文化从以修理为中心转变到以可靠性为中心(续)显示文摘四、生产与维修之间建立密切的关系
两个企业,一个达到以可靠性为中心的文化,而另一个则没有达到,他们的另一个明显差别在于生产人员与维修人员之间的团队合作水平。参照图1可知,在被动维修环境中,生产与维修之间的关系十分简单。生产说“起跳”,维修就说“多高”?另外,有些人对这种情况称为“面向用户”。我倒认为这是“生产奇想的奴隶”。 | Alexander(Sandy) Dunn 王秩信(译) | 2009 | 设备管理与维修2009，，11： | 0 |
| 17 | 企业文化从以修理为中心转变到以可靠性为中心显示文摘本文将讨论5个关键要素,它们是从传统的以修理为中心的企业文化成功地转变为以主动的、可靠性为中心的企业文化所需要的,并讨论提高设备性能和人员素质两方面所取得的回报。这5个要素是:(1)保证有一个长期的战略目标;(2)奖励制度与战略目标相结合;(3)生产与维修之间建立密切的合作;(4)创建团队合作和企业学习的机会;(5)坚强且有献身精神的领导。根据我们的经验,大多数企业文化改革初创者的失败原因是由于没有具备上述所有这些要素。 | Alexander(sandy) Dunn 王秩信(译) | 2009 | 设备管理与维修2009，，10： | 0 |
| 18 | 生物细胞中的能量学和力学显示文摘世界上的生物总是在不停地运动着。动物在奔跑、飞翔、游泳。植物日复一日追逐着太阳。即使是微生物,也在不停地运动。我们体内的细胞的各个部分也在不停地运动,这些运动使我们的细胞可以生长、分裂、改变形状、甚至运动。除了运动之外,我们的身体还必须能够感知到周围的世界。活体细胞可以对周围很多种机械刺激产生反馈,比如伸展,液体的流动,渗透压的改变,周围环境的硬度。我们的触觉和听觉要求细胞能够感知非常细微的机械力。我们对血压的调节能力依赖于分布在体内动脉和静脉的机械敏感性。 | 张萌 李国辉 Alexander R. Dunn Andrew Price | 2015 | 物理2015，0，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员