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Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials

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npj Computational Materials 2020年第1期6卷 964-970页

作者： Yabo dan Yong Zhao Xiang Li Shaobo Li Ming Hu Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyang 550025China Department of Computer Science and Engineering University of South CarolinaColumbiaSC 29201USA Key Laboratory of Advanced Manufacturing Technology Ministry of EducationGuizhou UniversityGuiyang 550025China Department of Mechanical Engineering University of South CarolinaColumbiaSC 29201USA

A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired *** effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials ***,we propose a generative machine learning model(MatGAN)based on a generative adversarial network(GAN)for efficient generation of new hypothetical inorganic *** with materials from the ICSD database,our GAN model can generate hypothetical materials not existing in the training dataset,reaching a novelty of 92.53% when generating 2 million *** percentage of chemically valid(charge-neutral and electronegativitybalanced)samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD,even though no such chemical rules are explicitly enforced in our GAN model,indicating its capability to learn implicit chemical composition rules to form *** algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials.

关键词： chemical inverse networks

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氢燃料电池用催化剂的制备和性能研究

工业催化 2024年第4期32卷 50-55页

作者：颜攀敦邱晨曦陈丹张洁兰西安凯立新材料股份有限公司陕西西安710201 新型贵金属催化剂研发技术国家地方联合工程研究中心陕西西安710201 陕西省催化材料与技术重点实验室陕西西安710201 西安市催化材料与技术重点实验室陕西西安710201

氢燃料电池是一种能够将化学能直接转化为电能的能量转化装置,具有转化效率高、清洁环保等优点,被认为是21世纪最有前途的新能源技术。催化剂作为氢燃料电池的关键材料,直接影响电池的使用寿命。通过以炭黑为基体、血晶素为前驱体,通过... 详细信息

氢燃料电池是一种能够将化学能直接转化为电能的能量转化装置,具有转化效率高、清洁环保等优点,被认为是21世纪最有前途的新能源技术。催化剂作为氢燃料电池的关键材料,直接影响电池的使用寿命。通过以炭黑为基体、血晶素为前驱体,通过自组装热解两步法制备复合载体,并负载铂制备Pt/Fe-N-C/EC复合催化剂。考察催化剂的电化学性能,并将其制备成膜电极组装单电池测试电池性能。结果表明,制备的铂炭催化剂具有良好的电化学性能,质量比活性可达183.26 mA·mg^(-1),电化学活性面积可达92.79 m^(2)·g^(-1),膜电极组装的单池在doe.速耐久30000圈实验后,其在0.8 A·cm^(-2)处电压值衰减18 mV,电化学活性面积衰减37.64%,符合doe.准。

关键词：能源化学氢燃料电池铂炭催化剂 Fe-N-C 膜电极性能

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An Interactive Visual Analytics Framework for Multi-Field Data in a Geo-Spatial Context

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Tsinghua Science and Technology 2013年第2期18卷 111-124页

作者： Zhiyuan Zhang Xiaonan Tong Kevin T. McDonnell Alla Zelenyuk dan Imre Klaus Mueller the Visual Analytics and Imaging Lab at Computer Science Department Stony Brook University Stony Brook NY 11790 USA an undergraduate student at Stanford University Stanford CA 94305 USA the Department of Mathematics Dowling College Oakdale NY 11769 USA Pacific Northwest National Lab Richland WA 99354 USA also with SUNY Korea

Climate research produces a wealth of multivariate data. These data often have a geospatial reference and so it is of interest to show them within their geospatial context. One can consider this configuration as a multifield visualization problem, where the geo-space provides the expanse of the field. However, there is a limit on the amount of multivariate information that can be fit within a certain spatial location, and the use of linked multivariate information displays has previously been devised to bridge this gap. In this paper we focus on the interactions in the geographical display, present an implementation that uses Google Earth, and demonstrate it within a tightly linked parallel coordinates display. Several other visual representations, such as pie and bar charts are integrated into the Google Earth display and can be interactively manipulated. Further, we also demonstrate new brushing and visualization techniques for parallel coordinates, such as fixed-window brushing and correlation-enhanced display. We conceived our system with a team of climate researchers, who already made a few important discoveries using it. This demonstrates our system＇s great potential to enable scientific discoveries, possibly also in other domains where data have a geospatial reference.

关键词： geospatial visualization visual analytics information visualization multivariate visualization parallel coordinates coordinated displays linking and brushing

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Tracking materials science data lineage to manage millions of materials experiments and analyses

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npj Computational Materials 2019年第1期5卷 460-468页

作者： Edwin Soedarmadji Helge S.Stein Santosh K.Suram dan Guevarra John M.Gregoire Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCAUSA Toyota Research Institute Los AltosCA 94022USA

In an era of rapid advancement of algorithms that extract knowledge from data,data and metadata management are increasingly critical to research *** materials science,there are few examples of experimental databases that contain many different types of information,and compared with other disciplines,the database sizes are relatively *** these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments,which we address with the development of a lightweight data management framework that is generally applicable for experimental science and *** years of managing experiments with this system has yielded the Materials Experiment and Analysis Database(MEAD)that contains raw data and metadata from millions of materials synthesis and characterization experiments,as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source *** unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing *** search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their *** enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences,and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

关键词： linking distillation autonomous

A peroxidase-like single-atom Fe-N_(5)active site for effective killing human lung adenocarcinoma cells

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Nano Research 2023年第4期16卷 5216-5225页

作者： Liye Zhu Hong Zhong dan Du Tao Li Hoai Nguyen Scott PBeckman Wentao Xu Jin-Cheng Li Nan Cheng Yuehe Lin Beijing Laboratory for Food Quality and Safety College of Food Science and Nutritional EngineeringChina Agricultural UniversityBeijing 100083China School of Mechanical and Materials Engineering Washington State UniversityPullmanWA 99164USA Faculty of Chemical Engineering Yunnan Provincial Key Laboratory of Energy Saving in Phosphorus Chemical Engineering and New Phosphorus MaterialsKunming University of Science and TechnologyKunming 650500China Department of Chemistry and Biochemistry Northern Illinois University1425 W.Lincoln Hwy.DeKalbIL 60115USA X-ray Science Division Argonne National Laboratory9700 S.Cass AveLemontIL 60439USA Material Science Division Argonne National Laboratory9700 S.Cass AveLemontIL 60439USA

Single-atom catalyst(SAC)is one of the newest catalysts,and attracts people’s wide attention in cancer therapy based on their characteristics of maximum specific catalytic activity and high *** designed and synthesized a Fe-N decorated graphene nanosheet(Fe-N5/GN SAC)with the coordination number of *** enzymology and theoretical calculations,the Fe-N5/GN SAC has outstanding intrinsic peroxidase-like catalytic activity due to single-atom Fe site with five-N-coordination *** explored its potential on lung cancer therapy,and found that it could kill human lung adenocarcinoma cells(A549)by decomposing hydrogen peroxide(H_(2)O_(2))into toxic reactive oxygen species(ROS)under acidic microenvironment in threedimensional(3D)lung cancer cell *** study demonstrates a promising application of SAC with highly efficient single-atom catalytic sites for cancer treatment.

关键词： single-atom catalyst three-dimensional(3D) cancer cell model peroxidase-like catalytic activity cancer therapy

Analyzing machine learning models to accelerate generation of fundamental materials insights

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npj Computational Materials 2019年第1期5卷 858-866页

作者： Mitsutaro Umehara Helge S.Stein dan Guevarra Paul F.Newhouse David A.Boyd John M.Gregoire Joint Center for Artificial Photosynthesis California Institute of TechnologyPasadenaCA 91125USA Future Mobility Research Department Toyota Research Institute of North AmericaAnn ArborMI 48105USA

Machine learning for materials science envisions the acceleration of basic science research through automated identification of key data relationships to augment human interpretation and gain scientific understanding.A primary role of scientists is extraction of fundamental knowledge from data,and we demonstrate that this extraction can be accelerated using neural networks via analysis of the trained data model itself rather than its application as a prediction *** neural networks excel at modeling complex data relationships in multi-dimensional parameter spaces,such as that mapped by a combinatorial materials science *** a performance metric in a given materials space provides direct information about(locally)optimal materials but not the underlying materials science that gives rise to the variation in *** building a model that predicts performance(in this case photoelectrochemical power generation of a solar fuels photoanode)from materials parameters(in this case composition and Raman signal),subsequent analysis of gradients in the trained model reveals key data relationships that are not readily identified by human inspection or traditional statistical *** interpretation of these key relationships produces the desired fundamental understanding,demonstrating a framework in which machine learning accelerates data interpretation by leveraging the expertize of the human *** also demonstrate the use of neural network gradient analysis to automate prediction of the directions in parameter space,such as the addition of specific alloying elements,that may increase performance by moving beyond the confines of existing data.

关键词： neural prediction desired

Deuterium oxide dilution can be used to determine the net energy content of feeds for dairy cattle and goats

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Open Journal of Animal Sciences 2013年第3期3卷 219-224页

作者： dan L. Brown Department of Animal Science Cornell University New York USA

This article explores a technique for measuring the energy (NEL) value of feeds without respiration calorimetry or slaughter. The objectives were to compare results obtained from goats with those from cows, and to describe factors which limited the precision of these NEL estimates. One lactating Alpine doe.and one lactateing Holstein cow were assigned to each of six different sequences of three 56-d feeding treatments consisting of low, medium, and high doses of the basal diet, rice bran, or hominy feed. This resulted in 30 observations of the basal diet and 12 of each byproduct feed for each species and utilized a total of 18 cows and 18 doe.. The NEL values of the basal diet, rice bran, and hominy feed were calculated as the sum of milk energy, change in body energy, and estimated fasting heat production per kilogram of feed dry matter. Milk energy was determined by bomb calorimetry and body energy from live body weight and deuterium oxide space. The NEL (MJ/kg) determined in this manner were basal diet, 5.73 and 5.98;rice bran, 7.11 and 7.07;and hominy feed, 6.99 and 8.20 for cows and goats, respectively.

关键词： Net Energy Deuterium Dairy Goats Cattle

Expanding the application of a UV-visible reporter for transient gene expression and stable transformation in plants

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Horticulture Research 2021年第1期8卷 3286-3296页

作者： Guoliang Yuan Haiwei Lu dan Tang Md Mahmudul Hassan Yi Li Jin-Gui Chen Gerald A.Tuskan Xiaohan Yang Bioscie nces Divisi on Oak Ridge Nati onal Lab oratoryOak RidgeTN 37831USA The Center for Bioe nergy Inno vati on Oak Ridge Nati onal Lab oratoryOak RidgeTN 37831USA Department of Plant Science and Landscape Architecture University of ConnecticutStorrsCT 06269USA National Center for Citrus Improvement College of HorticultureHunan Agricultural UniversityChangsha 410128 HunanChina Department of Genetics and Plant breeding Patuakhali Science and Technology UniversityDumkiPatuakhali 8602Bangladesh

Green fl uorescent protein(GFP)has been widely used for monitoring gene expression and protein localization in diverse ***,highly sensitive imaging equipment,like fl uorescence microscope,is usually required for the visualization of GFP,limitings its application to fi xed locations in samples.A reporter that can be visualized in realtime regardless the shape,size and location of the target samples will increase the fl exibility and ef fi ciency of research ***,we report the application of a GFP-like protein,called eYGFPuv,in both transient expression and stable transformation,in two herbaceous plant species(Arabidopsis and tobacco)and two woody plant species(poplar and citrus).We observed bright fl uorescence under UV light in all of the four plant species without any effects on plant growth or *** was shown to be effective for imaging transient expression in leaf and root *** a focus on in vitro transformation,we demonstrated that the transgenic events expressing 1x eYGFPuv could be easily identi fi ed visually during the callus stage and the shoot stage,enabling early and ef fi cient selection of ***,whole-plant level visualization of eYGFPuv revealed its ubiquitous stability in transgenic *** addition,our transformation experiments showed that eYGFPuv can also be used to select transgenic plants without *** work demonstrates the feasibility of utilizing 1x eYGFPuv in studies of gene expression and plant transformation in diverse plants.

关键词： transient transformation application

Synthesis of Polycrystalline Diamond Compact with Selenium:Discovery of a New Se–C Compound

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Chinese Physics Letters 2020年第5期37卷 102-105页

作者： Wen-dan Wang Ao Li Guo-Heng Xu Pei Wang Yue-Gao Liu Li-Ping Wang School of Physical Science and Technology Southwest Jiaotong UniversitChengdu 610031 Materials Research Center Institute of Modern PhysicsChinese Acadermy of SciencesLanzhou 730000 Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology(SUSTech)Shenzhen 518055 CAS Key Laboratory for Experimental Study under Deep-Sea Extreme Conditions Institute of Deep-Sea Science and EngineeringChinese Academy of SciencesSanya 572000

Sintering of polycrystalline diamond with selenium was investigated under pressure of 6.5-10.5 GPa at a constant temperature of 1850℃.A new carbon-selenium compound with a most plausible chemical formula of SeC and a WC-type hexagonal structure(space group P6m2)has been discovered in the recovered samples sintered at 10.5 GPa and 1850℃.Refined lattice parameters are as follows:a=2.9277(4)A,c=2.8620(4)A,V=21.245(4)A^*** diamond compacts hot-pressed at 10.5 GPa have excellent mechanical properties with a Vickers hardness of about 68 GPa at a loading force of 19.6 *** intergrowths observed in these samples may have benefited from the catalytic effects of Se/SeC on the nucleation and crystal growth of diamond.

关键词： Diamond hexagonal Vickers

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