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| 1 | Machine-learned interatomic potentials by active learning:amorphous and liquid hafnium dioxide显示文摘We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential(GAP).Our active learning scheme consists of an unsupervised machine learning(ML)scheme coupled with a Bayesian optimization technique that evaluates the GAP model.We apply this scheme to a Hafnium dioxide(HfO2)dataset generated from a“melt-quench”ab initio molecular dynamics(AIMD)protocol.Our results show that the active learning scheme,with no prior knowledge of the dataset,is able to extract a configuration that reaches the required energy fit tolerance.Further,molecular dynamics(MD)simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO2 with near ab initio precision and quench rates(i.e.,1.0 K/ps)not accessible via AIMD.The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment.In addition,the calculated diffusion constants are in good agreement with previous ab initio studies. | Ganesh Sivaraman Anand Narayanan Krishnamoorthy Matthias Baur Christian Holm Marius Stan Gábor Csányi Chris Benmore Álvaro Vázquez-Mayagoitia | 2020 | npj Computational Materials2020，，1： | 10 |
| 2 | Recent advances and applications of deep learning methods in materials science显示文摘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 modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.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 methods.In 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 processing.For 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 datasets.We 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. | 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 | 2022 | npj Computational Materials2022，，1： | 9 |
| 3 | 针对患者利益的机器学习和人工智能研究:在透明性、可重复性、伦理和有效性等方面的20个关键问题显示文摘机器学习(ML)、人工智能(AI)和其他现代统计方法正为利用先前尚未开发且极速增长的数据资源提供新的机会,以期让患者获益。尽管目前正在进行许多有前景的研究,特别是在图像方面,但就文献整体而言还缺乏透明度、对可重复性清晰的阐述、对潜在伦理问题的探究,以及对有效性的明确验证。这些问题的存在有许多原因,其中最重要的一点(为此我们提供了初步解决方案)就是当前缺乏针对ML和AI的最佳实践指南。我们认为从事研究的跨学科团队和应用ML/AI影响健康的项目,将因解决有关透明度、可重复性、伦理和有效性(TREE)的一系列问题而受益。这里提出的20个关键问题为研究团队提供了一个研究设计、实施和报告框架;帮助编辑和同行评审专家评估文献的贡献;让患者、临床医生和政策制定者评估新发现可能会给患者带来的获益。 | Sebastian Vollmer Bilal A Mateen Gergo Bohner Franz J Kirdly Rayid Ghani Pall Jonsson Sarah Cumbers Adrian Jonas Katherine S L McAllister Puja Myles David Granger Mark Birse Richard Branson Karel G M Moons Gary S Collins John P A Chris Holmes Harry Hemingwayp 李峰(译) 徐磊(校) 赵邑(校) | 2020 | 英国医学杂志中文版2020，23，9： | 5 |
| 4 | An evolutionary treasure:unification of a broad set of amidohydrolases related to urease显示文摘 | Liisa Holm Chris Sander | 1997 | Proteins1997，28，1： | 1 |
| 5 | The application of Operation and Technology Roadmapping to aid Singaporean SMEs identify and select emerging technologies 显示文摘 | Chris Holmes Mike Ferrill | 2005 | Technological Forecasting and Social Change2005，72，3： | 1 |
| 6 | Mapping the protein universe显示文摘 | Holm Liisa Sander Chris | 1996 | Science1996，273，5275： | 1 |
| 7 | Protein structure comparison by alignment of distance matrices显示文摘 | Holm Liisa Sander Chris | 1993 | Journal of Molecular Biology1993，233，1： | 1 |
| 8 | Perfect sampling for the wavelet reconstruction of signals 显示文摘 | Chris Holmes David G T Dension | 2002 | IEEE Transaction on Signal Processing2002，50，2： | 1 |
| 9 | Computerized method of visual acuity testing: adaptation of the amblyopia treatment study visual acuity testing protocol 1 1 Additional technical information about the Electronic Visual Acuity Tester and the Amblyopia Treatment Study visual acuity testing显示文摘 | Pamela S. Moke Andrew H. Turpin Roy W. Beck Jonathan M. Holmes Michael X. Repka Eileen E. Birch Richard W. Hertle Raymond T. Kraker Joseph M. Miller Chris A. Johnson | 2001 | American Journal of Ophthalmology2001，，6： | 1 |
| 10 | The application of Operation and Technology Roadmapping to aid Singaporean SMEs identify and select emerging technologies显示文摘 | Chris Holmes Mike Ferrill | 2005 | Technological F orecasting and Social Change2005，，3： | 1 |
| 11 | The application of Operation and Technology Roadmapping to aid Singaporean SMEs identify and select emerging technologies 显示文摘 | Chris Holmes and Mike Ferrill | 2005 | Technological Forecasting and Social Change2005，，3： | 1 |
| 12 | 姐妹团:逆水行舟,成为战地英雄的女医生们显示文摘1914年,一批女医生因性别问题在战争中未能为英军提供医疗服务,她们遭拒后横跨英吉利海峡前往法国实施救助。Chris Holme运用新发现的文献追溯了她们的故事。1914年夏天,英国战争办公室拒绝了一名苏格兰医生Elsie Inglis提出的建立一所完全由女性成员组成的军事医院的建议。她得到的回复是“回家好好待着吧”。 | Chris Holme 陈俊妍(译) | 2020 | 英国医学杂志中文版2020，23，10： | 0 |
| 13 | A general-purpose material property data extraction pipeline from large polymer corpora using natural language processing显示文摘The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from literature.We used natural language processing methods to automatically extract material property data from the abstracts of polymer literature.As a component of our pipeline,we trained MaterialsBERT,a language model,using 2.4 million materials science abstracts,which outperforms other baseline models in three out of five named entity recognition datasets.Using this pipeline,we obtained~300,000 material property records from~130,000 abstracts in 60 hours.The extracted data was analyzed for a diverse range of applications such as fuel cells,supercapacitors,and polymer solar cells to recover non-trivial insights.The data extracted through our pipeline is made available at polymerscholar.org which can be used to locate material property data recorded in abstracts.This work demonstrates the feasibility of an automatic pipeline that starts from published literature and ends with extracted material property information. | Pranav Shetty Arunkumar Chitteth Rajan Chris Kuenneth Sonakshi Gupta Lakshmi Prerana Panchumarti Lauren Holm Chao Zhang Rampi Ramprasad | 2023 | npj Computational Materials2023，，1： | 0 |
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