Pub Date : 2022-11-02DOI: 10.1080/08940886.2022.2159711
A. D. Rosa, I. Kupenko, J. Hernández, A. Forestier, M. Muñoz, G. Morard, M. A. Bouhifd, K. Lomachenko, R. Torchio, A. Chumakov, O. Mathon, M. Mezouar
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{"title":"New Opportunities for Earth Science at the Extremely Brilliant Source of the European Synchrotron Radiation Facility","authors":"A. D. Rosa, I. Kupenko, J. Hernández, A. Forestier, M. Muñoz, G. Morard, M. A. Bouhifd, K. Lomachenko, R. Torchio, A. Chumakov, O. Mathon, M. Mezouar","doi":"10.1080/08940886.2022.2159711","DOIUrl":"https://doi.org/10.1080/08940886.2022.2159711","url":null,"abstract":"of","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"8 - 16"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47526327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-02DOI: 10.1080/08940886.2022.2161790
J. Lüning
More than 400 researchers, primarily from Germany, but also from Europe and countries further abroad, gathered for the SNI2022 conference in Berlin, which took place at the Freie Universität Berlin from September 5 to 7, 2022. The participants’ common denominator is their expertise and great interest in using advanced experimental techniques uniquely provided by large-scale research facilities. The research interests cover a broad variety of scientific fields, the goals encompassing everything from curiosity-driven investigations of fundamental principles to application-oriented development of materials and devices. The SNI2022 conference therefore provided an overview of the breadth of scientific and technological areas as well as innovation and technology development to which research with synchrotron radiation, neutrons, and ion beams at large-scale facilities contributes. These facilities provide scientists from universities, research institutions, and industry with world-leading, and in many cases even world-wide unique experimental capabilities; for example, revealing the finest details of structural and functional properties, following their evolution in situ and during operation under relevant conditions, and resolving the dynamics of fundamental processes. Tailoring of the facilities’ instruments to specific needs has in many cases been made possible thanks to funding of the Federal Ministry of Education and Research (BMBF) within the ErUM framework (Investigation of the Universe and Matter), an internationally recognized tool fostering collaborations between university research groups and facilities.
{"title":"SNI2022: German Conference for Research with Synchrotron Radiation, Neutrons, and Ion Beams at Large-Scale Facilities","authors":"J. Lüning","doi":"10.1080/08940886.2022.2161790","DOIUrl":"https://doi.org/10.1080/08940886.2022.2161790","url":null,"abstract":"More than 400 researchers, primarily from Germany, but also from Europe and countries further abroad, gathered for the SNI2022 conference in Berlin, which took place at the Freie Universität Berlin from September 5 to 7, 2022. The participants’ common denominator is their expertise and great interest in using advanced experimental techniques uniquely provided by large-scale research facilities. The research interests cover a broad variety of scientific fields, the goals encompassing everything from curiosity-driven investigations of fundamental principles to application-oriented development of materials and devices. The SNI2022 conference therefore provided an overview of the breadth of scientific and technological areas as well as innovation and technology development to which research with synchrotron radiation, neutrons, and ion beams at large-scale facilities contributes. These facilities provide scientists from universities, research institutions, and industry with world-leading, and in many cases even world-wide unique experimental capabilities; for example, revealing the finest details of structural and functional properties, following their evolution in situ and during operation under relevant conditions, and resolving the dynamics of fundamental processes. Tailoring of the facilities’ instruments to specific needs has in many cases been made possible thanks to funding of the Federal Ministry of Education and Research (BMBF) within the ErUM framework (Investigation of the Universe and Matter), an internationally recognized tool fostering collaborations between university research groups and facilities.","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"17 - 19"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48732081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-02DOI: 10.1080/08940886.2022.2156752
R. Laasch, Zhenxian Liu, Lu Ma, Sarah Nicholas, P. Northrup, J. Thieme, M. Whitaker
From extraterrestrial materials to minerals under high pressure deep in Earth’s interior, scientists study the various natural materials and in-terconnected systems of our planet to better understand its origin and evolution, current processes and cycles, and future. This understanding is essential for access to natural resources, preservation of Earth’s critical environment, response to climate change, and protection from natural hazards. To answer important and large-scale questions about our environment, our planet, and our solar system, the keys are often found at the microscale of chemical and physical properties and processes. However, these questions are often so complex that researchers need highly specialized tools to access the intricate details of their samples. As a U.S. Department of Energy (DOE) Office of Science user facil - ity located at DOE’s Brookhaven National Laboratory, the National Synchrotron Light Source II (NSLS-II) provides a wide range of capabilities ideal for studying geological samples. The following highlights describe application of several beamlines at NSLS-II, covering a range of spatial and energy scales, for geological research.
{"title":"From Outer Space to the Center of the Earth: How NSLS-II Capabilities Enable Geoscience Studies","authors":"R. Laasch, Zhenxian Liu, Lu Ma, Sarah Nicholas, P. Northrup, J. Thieme, M. Whitaker","doi":"10.1080/08940886.2022.2156752","DOIUrl":"https://doi.org/10.1080/08940886.2022.2156752","url":null,"abstract":"From extraterrestrial materials to minerals under high pressure deep in Earth’s interior, scientists study the various natural materials and in-terconnected systems of our planet to better understand its origin and evolution, current processes and cycles, and future. This understanding is essential for access to natural resources, preservation of Earth’s critical environment, response to climate change, and protection from natural hazards. To answer important and large-scale questions about our environment, our planet, and our solar system, the keys are often found at the microscale of chemical and physical properties and processes. However, these questions are often so complex that researchers need highly specialized tools to access the intricate details of their samples. As a U.S. Department of Energy (DOE) Office of Science user facil - ity located at DOE’s Brookhaven National Laboratory, the National Synchrotron Light Source II (NSLS-II) provides a wide range of capabilities ideal for studying geological samples. The following highlights describe application of several beamlines at NSLS-II, covering a range of spatial and energy scales, for geological research.","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"2 - 7"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43039785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-02DOI: 10.1080/08940886.2022.2159713
Sintu Rongpipi
{"title":"ALS User Meeting and Workshops 2022 ALS User Meeting Highlights","authors":"Sintu Rongpipi","doi":"10.1080/08940886.2022.2159713","DOIUrl":"https://doi.org/10.1080/08940886.2022.2159713","url":null,"abstract":"","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"20 - 23"},"PeriodicalIF":0.0,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44669045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-12DOI: 10.1080/08940886.2022.2112498
Nathan C. Drucker, Tongtong Liu, Zhantao Chen, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Thanh Nguyen, Yao Wang, Mingda Li
Vol. 35, No. 4, 2022, Synchrotron radiation newS Feature article Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering NathaN C. DruCker,1,2 toNgtoNg Liu,1,3 ZhaNtao CheN,1,4 ryotaro okabe,1,5 abhijatmeDhi ChotrattaNapituk1,6 thaNh NguyeN,1,7 yao WaNg,8 aND miNgDa Li1,7 1Quantum Measurement Group, MIT, Cambridge, Massachusetts, USA 2School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA 3Department of Physics, MIT, Cambridge, Massachusetts, USA 4Department of Mechanical Engineering, MIT, Cambridge, Massachusetts, USA 5Department of Chemistry, MIT, Cambridge, Massachusetts, USA 6Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA 7Department of Nuclear Science and Engineering, MIT, Cambridge, MA, USA 8Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, USA Introduction Machine learning has been highly successful in boosting the research for neutron and X-ray scattering in the past few years [1, 2]. For diffraction, machine learning has shown great promise in phase mapping [3, 4] and crystallographic information determination [5, 6]. In small-angle scattering, machine learning shows the power in reaching super-resolution [7, 8], reconstructing structures for macromolecules [9], and building structure-property relations [10]. As for absorption spectroscopy, machine learning has enabled the rapid inverse search for optimized structures [11, 12] with improved spectral interpretability [13, 14]. Overall, as a data-driven approach, the success of the machinelearning-based scattering analysis depends on a few criteria, including:
NathaN C. DruCker,1,2刘彤彤，1,3陈占涛，1,4 okabe ryotaro,1,5 abhijatmeDhi chotrattanapituk1,6 NguyeN thaNh,1,7 WaNg yao,8 and limingda 1,7量子测量组，麻省理工学院，剑桥，马萨诸塞州，美国2哈佛大学工程与应用科学学院，剑桥，美国3麻省理工学院，剑桥，物理系美国马萨诸塞州4美国马萨诸塞州剑桥市麻省理工学院机械工程系5美国马萨诸塞州剑桥市麻省理工学院化学系6美国马萨诸塞州剑桥市麻省理工学院电气工程与计算机科学系7美国马萨诸塞州剑桥市麻省理工学院核科学与工程系8南卡罗来纳州克莱姆森大学物理与天文系;在过去的几年中，机器学习在促进中子和x射线散射研究方面取得了很大的成功[1,2]。对于衍射，机器学习在相映射[3,4]和晶体学信息确定[5,6]方面显示出很大的前景。在小角度散射中，机器学习在达到超分辨率[7,8]，重建大分子[9]的结构以及建立结构-性质关系[10]方面显示出强大的能力。在吸收光谱方面，机器学习使优化结构的快速逆搜索成为可能[11,12]，提高了光谱的可解释性[13,14]。总的来说，作为一种数据驱动的方法，基于机器学习的散射分析的成功取决于几个标准，包括:
{"title":"Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering","authors":"Nathan C. Drucker, Tongtong Liu, Zhantao Chen, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Thanh Nguyen, Yao Wang, Mingda Li","doi":"10.1080/08940886.2022.2112498","DOIUrl":"https://doi.org/10.1080/08940886.2022.2112498","url":null,"abstract":"Vol. 35, No. 4, 2022, Synchrotron radiation newS Feature article Challenges and Opportunities of Machine Learning on Neutron and X-ray Scattering NathaN C. DruCker,1,2 toNgtoNg Liu,1,3 ZhaNtao CheN,1,4 ryotaro okabe,1,5 abhijatmeDhi ChotrattaNapituk1,6 thaNh NguyeN,1,7 yao WaNg,8 aND miNgDa Li1,7 1Quantum Measurement Group, MIT, Cambridge, Massachusetts, USA 2School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA 3Department of Physics, MIT, Cambridge, Massachusetts, USA 4Department of Mechanical Engineering, MIT, Cambridge, Massachusetts, USA 5Department of Chemistry, MIT, Cambridge, Massachusetts, USA 6Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, USA 7Department of Nuclear Science and Engineering, MIT, Cambridge, MA, USA 8Department of Physics and Astronomy, Clemson University, Clemson, South Carolina, USA Introduction Machine learning has been highly successful in boosting the research for neutron and X-ray scattering in the past few years [1, 2]. For diffraction, machine learning has shown great promise in phase mapping [3, 4] and crystallographic information determination [5, 6]. In small-angle scattering, machine learning shows the power in reaching super-resolution [7, 8], reconstructing structures for macromolecules [9], and building structure-property relations [10]. As for absorption spectroscopy, machine learning has enabled the rapid inverse search for optimized structures [11, 12] with improved spectral interpretability [13, 14]. Overall, as a data-driven approach, the success of the machinelearning-based scattering analysis depends on a few criteria, including:","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"16 - 20"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42317975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-07DOI: 10.1080/08940886.2022.2114723
Richard J. Gildea, C. Orr, N. Paterson, D. Hall
Historically, solving the structure of a protein required deep knowledge of crystallography and the ability to produce protein crystals of suitable quality to generate high-quality diffraction data. Over the years, as beamline optics, end-stations, detectors, and data collection strategies have improved, it has become more feasible to extract highquality diffraction data from ever smaller or less perfect protein crystals and from very large arrays of crystals for techniques such as serial synchrotron crystallography and fragment-based drug discovery. At Diamond, these improvements have been coupled with highly integrated automated pipelines for data reduction and structure solution using techniques such as molecular replacement and experimental phasing. This has led to the dichotomy, and benefits, of being able to do increasingly challenging experiments requiring deep crystallographic knowledge with facility staff support at the same time as lowering the barrier to entry where automated structure solution tools of the facility perform this task for those scientists with less experience. This enables users to focus on the science rather than the process. Diamond Light Source, the UK’s national synchrotron, has a suite of instruments dedicated to solving the 3D structure of large biological molecules, including seven macromolecular crystallography (MX) beamlines. Solved 3D structures are deposited into the publicly available Protein Data Bank (PDB) and the depositions are released on a weekly basis. In 2020, following 13 years of operation, Diamond hit the milestone of 10,000 structures deposited in the PDB. Two years on, this number is now more than 12,000. Thanks to decades of work across the world, there is an ocean of information in the PDB that serves as an invaluable reference when solving the structures of new proteins.
{"title":"Embedding AI in the Protein Crystallography Workflow","authors":"Richard J. Gildea, C. Orr, N. Paterson, D. Hall","doi":"10.1080/08940886.2022.2114723","DOIUrl":"https://doi.org/10.1080/08940886.2022.2114723","url":null,"abstract":"Historically, solving the structure of a protein required deep knowledge of crystallography and the ability to produce protein crystals of suitable quality to generate high-quality diffraction data. Over the years, as beamline optics, end-stations, detectors, and data collection strategies have improved, it has become more feasible to extract highquality diffraction data from ever smaller or less perfect protein crystals and from very large arrays of crystals for techniques such as serial synchrotron crystallography and fragment-based drug discovery. At Diamond, these improvements have been coupled with highly integrated automated pipelines for data reduction and structure solution using techniques such as molecular replacement and experimental phasing. This has led to the dichotomy, and benefits, of being able to do increasingly challenging experiments requiring deep crystallographic knowledge with facility staff support at the same time as lowering the barrier to entry where automated structure solution tools of the facility perform this task for those scientists with less experience. This enables users to focus on the science rather than the process. Diamond Light Source, the UK’s national synchrotron, has a suite of instruments dedicated to solving the 3D structure of large biological molecules, including seven macromolecular crystallography (MX) beamlines. Solved 3D structures are deposited into the publicly available Protein Data Bank (PDB) and the depositions are released on a weekly basis. In 2020, following 13 years of operation, Diamond hit the milestone of 10,000 structures deposited in the PDB. Two years on, this number is now more than 12,000. Thanks to decades of work across the world, there is an ocean of information in the PDB that serves as an invaluable reference when solving the structures of new proteins.","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"51 - 54"},"PeriodicalIF":0.0,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48526039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-28DOI: 10.1080/08940886.2022.2114702
Silvana Westbury
{"title":"Marking 75 Years of Science with Synchrotron Light","authors":"Silvana Westbury","doi":"10.1080/08940886.2022.2114702","DOIUrl":"https://doi.org/10.1080/08940886.2022.2114702","url":null,"abstract":"","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"55 - 56"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41869862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-22DOI: 10.1080/08940886.2022.2112500
C. Benmore, Tekin Bicer, M. Chan, Z. Di, Dogˇa Gürsoy, In-hui Hwang, N. Kuklev, Dergan Lin, Zhengchun Liu, I. Lobach, Zhi-wei Qiao, L. Rebuffi, Hemant Sharma, Xianbo Shi, Cheng-wei Sun, Yudong Yao, T. Zhou, A. Sandy, A. Miceli, Yin-e Sun, N. Schwarz, M. Cherukara
To address this, we have designed a fully integrated digital twin environment for simulation and debugging based on experimentally collected data. We have been able to show that incorporating time and physics-based biasing can improve long-term stability and robustness sufficiently for long-term use. Resulting ML libraries were interfaced with a Tcl/Tk production control system via an SDDS-compatible interface, which allowed for easy integration and immediate operational use. Experimental benchmarks have demonstrated new methods to be faster than existing ones in recovering full performance of the accelerator after a perturbation.
{"title":"Advancing AI/ML at the Advanced Photon Source","authors":"C. Benmore, Tekin Bicer, M. Chan, Z. Di, Dogˇa Gürsoy, In-hui Hwang, N. Kuklev, Dergan Lin, Zhengchun Liu, I. Lobach, Zhi-wei Qiao, L. Rebuffi, Hemant Sharma, Xianbo Shi, Cheng-wei Sun, Yudong Yao, T. Zhou, A. Sandy, A. Miceli, Yin-e Sun, N. Schwarz, M. Cherukara","doi":"10.1080/08940886.2022.2112500","DOIUrl":"https://doi.org/10.1080/08940886.2022.2112500","url":null,"abstract":"To address this, we have designed a fully integrated digital twin environment for simulation and debugging based on experimentally collected data. We have been able to show that incorporating time and physics-based biasing can improve long-term stability and robustness sufficiently for long-term use. Resulting ML libraries were interfaced with a Tcl/Tk production control system via an SDDS-compatible interface, which allowed for easy integration and immediate operational use. Experimental benchmarks have demonstrated new methods to be faster than existing ones in recovering full performance of the accelerator after a perturbation.","PeriodicalId":39020,"journal":{"name":"Synchrotron Radiation News","volume":"35 1","pages":"28 - 35"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41890925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-21DOI: 10.1080/08940886.2022.2112501
Allan Pinto, Gabriel Borin, Bruno Carlos, Matheus Bernardi, Matheus F. Sarmento, Alan Z. Peixinho, T. V. Spina, E. Miqueles
Vol. 35, No. 4, 2022, Synchrotron radiation newS Technical RepoRT Annotat3D: A Modern Web Application for Interactive Segmentation of Volumetric Images at Sirius/LNLS AllAn Pinto, GAbriel borin, bruno CArlos, MAtheus l. bernArdi, MAtheus F. sArMento, AlAn Z. Peixinho, thiAGo V. sPinA, And eduArdo x. Miqueles Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, Sao Paulo, Brazil
第35卷，2022年第4期，同步辐射新技术报告Annotat3D:在Sirius/LNLS AllAn Pinto、GAbriel borin、bruno CArlos、MAtheus l.bernArdi、MAtheous F.sArMento、AlAn Z.Peixinho、thiAGo V.sPinA和eduArdo x.Miqueles巴西同步光实验室（LNLS）进行体积图像交互式分割的现代网络应用，巴西能源与材料研究中心（CNPEM），巴西圣保罗坎皮纳斯
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