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Identification of chemical compositions from“featureless”optical absorption spectra:Machine learning predictions and experimental validations

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Nano Research 2023年第3期16卷 4188-4196页

作者： Tiankai Chen Jiali Li Pengfei Cai Qiaofeng Yao zekun ren Yixin Zhu Saif Khan Jianping Xie Xiaonan Wang Department of Chemical and Biomolecular Engineering National University of Singapore4 Engineering Drive 4Singapore 117585Singapore Department of Materials Science and Engineering National University of Singapore9 Engineering Drive 1Singapore 117575Singapore Singapore-MIT Alliance for Research and Technology(SMART) 1 CREATE WaySingapore 138602Singapore Department of Chemical Engineering Tsinghua UniversityBeijing 100084China

Rapid and accurate chemical composition identification is critically important in *** it can be achieved with optical absorption spectrometry by comparing the experimental spectra with the reference data when the chemical compositions are simple,such application is limited in more complicated scenarios especially in nano-scale *** is due to the difficulties in identifying optical absorption peaks(i.e.,from“featureless”spectra)arose from the *** this work,using the ultraviolet-visible(UV-Vis)absorption spectra of metal nanoclusters(NCs)as a demonstration,we develop a machine-learningbased method to unravel the compositions of metal NCs behind the“featureless”*** implementing a one-dimensional convolutional neural network,good matches between prediction results and experimental results and low mean absolute error values are achieved on these optical absorption spectra that human cannot *** work opens a door for the identification of nanomaterials at molecular precision from their optical properties,paving the way to rapid and high-throughput characterizations.

关键词： gold nanoclusters composition identification optical absorption machine learning convolutional neural network

A Novel Flux-weakening Control Method with Quadrature Voltage Constrain for Electrolytic Capacitorless PMSM Drives

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CES Transactions on Electrical Machines and Systems 2022年第3期6卷 279-287页

作者： Junya Huo Dawei Ding zekun ren Gaolin Wang Nannan Zhao Lianghong Zhu Dianguo Xu the School of Electrical Engineering and Automation Harbin Institute of TechnologyHarbin 150001China the Guangdong Midea Air-Conditioning Equipment Co. Ltd.Foshan 528311China IEEE

In electrolytic capacitorless permanent magnet synchronous motor(PMSM) drives, the DC-link voltage will fluctuate in a wide range due to the use of slim film capacitor. When the flux-weakening current is lower than-ψf/Ld during the high speed operation, the flux-weakening control loop will transform to a positive feedback mode, which means the reduction of flux-weakening current will lead to the acceleration of the voltage saturation, thus the whole system will be unstable. In order to solve this issue, this paper proposes a novel flux-weakening method for electrolytic capacitorless motor drives to maintain a negative feedback characteristic of the control loop during high speed operation. Based on the analysis of the instability mechanism in flux-weakening region, a quadrature voltage constrain mechanism is constructed to stabilize the ***, the parameters of the controller are theoretically designed for easier industrial application. The proposed algorithm is implemented on a 1.5 kW electrolytic capacitorless PMSM drive to verify the effectiveness of the flux-weakening performance.

关键词： Permanent magnet synchronous motor(PMSM) Electrolytic capacitorless drive Flux-weakening control Voltage constrain Stable operation

A sponge-based iron-tannic-acid hydrogel interface evaporator designed for clean water production

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Science China(Technological Sciences) 2024年第5期67卷 1579-1591页

作者： ZHANG zekun LI ChengLong TAO HuaYu GE Bo ZHANG YongLe ZHAO LiMin LIU JunChang ren GuiNa ZHANG ZhaoZhu School of Materials Science and Engineering Liaocheng UniversityLiaocheng252059China School of Environmental and Material Engineering Yantai UniversityYantai264405China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of SciencesLanzhou730000China

Due to the rapid advancement of technologies,there has been a significant increase in the discharge of industrial wastewater,and freshwater is becoming a scarce ***,research on solar evaporators is mainly focused on the efficient production of clean water,with less emphasis on the removal of residual pollutants remaining in the original *** this study,problems,including the difficult recovery of catalyst powder and the difficult removal of floating organic matter are solved by co-depositing low-surface-tension zirconia particles and bismuth tungstate onto the floating *** and melamine sponges were combined to solve the problem that traditional hydrogels lack mechanical *** excellent water-repellent effect can be seen from the contact angle between the liquid globule and canvas/felt,which is greater than 155°.The steam generation rate of the assembled evaporation system is 1.78 kg m^(-2)h^(-1),and its purification efficiency for methyl orange and rhodamine B exceeds 99%.This study presents a novel strategy for treating wastewater contaminated with organic dyes,aiming to solve problems including environmental damage,water pollution,and water scarcity.

关键词： oil removal non-wetting interface

Two-step machine learning enables optimized nanoparticle synthesis

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npj Computational Materials 2021年第1期7卷 498-507页

作者： Flore Mekki-Berrada zekun ren Tan Huang Wai Kuan Wong Fang Zheng Jiaxun Xie Isaac Parker Siyu Tian Senthilnath Jayavelu Zackaria Mahfoud Daniil Bash Kedar Hippalgaonkar Saif Khan Tonio Buonassisi Qianxiao Li Xiaonan Wang Department of Chemical and Biomolecular Engineering National University of SingaporeSingaporeSingapore Singapore-MIT Alliance for Research and Technology SMART SingaporeSingapore Institute for Infocomm Research Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore Institute of Materials Research&Engineering SingaporeSingapore Department of Materials Science and Engineering Nanyang Technological UniversitySingaporeSingapore Massachusetts Institute of Technology CambridgeMAUSA Department of Mathematics National University of SingaporeSingaporeSingapore Institute of High Performance Computing SingaporeSingapore

In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and *** this study,we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance *** a Gaussian process-based Bayesian optimization(BO)with a deep neural network(DNN),the algorithmic framework is able to converge towards the target spectrum after sampling 120 *** the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum,the DNN is used to predict the colour palette accessible with the reaction *** remaining interpretable by humans,the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties,such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

关键词： spectrum synthesis. synthesis

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

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npj Computational Materials 2019年第1期5卷 624-632页

作者： Felipe Oviedo zekun ren Shijing Sun Charles Settens Zhe Liu Noor Titan Putri Hartono Savitha Ramasamy Brian L.DeCost Siyu I.P.Tian Giuseppe Romano Aaron Gilad Kusne Tonio Buonassisi Massachusetts Institute of Technology CambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology Singapore 138602Singapore Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore National Institute of Standards and Technology MS 8520GaithersburgMD 20899USA

X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film *** propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD *** overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental *** a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and *** testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,*** propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of ***,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less.

关键词： neural networks dimensionality

Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains

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npj Computational Materials 2021年第1期7卷 1742-1751页

作者： Qiaohao Liang Aldair E.Gongora zekun ren Armi Tiihonen Zhe Liu Shijing Sun James R.Deneault Daniil Bash Flore Mekki-Berrada Saif A.Khan Kedar Hippalgaonkar Benji Maruyama Keith A.Brown John Fisher III Tonio Buonassisi Massachusetts Institute of Technology CambridgeMAUnited States Boston University BostonMAUnited States Singapore-MIT Alliance for Research and Technology SingaporeSingapore Air Force Research Laboratory DaytonOhioUnited States Agency for Science Technology and Research(A*STAR)SingaporeSingapore National University of Singapore SingaporeSingapore Present address:Aalto University EspooFinland Present address:Northwestern Polytechincal University(NPU) Xi’anShaanxi P.R.China

Bayesian optimization(BO)has been leveraged for guiding autonomous and high-throughput experiments in materials ***,few have evaluated the efficiency of BO across a broad range of experimental materials *** this work,we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials *** defining acceleration and enhancement metrics for materials optimization objectives,we find that surrogate models such as Gaussian Process(GP)with anisotropic kernels and Random Forest(RF)have comparable performance in BO,and both outperform the commonly used GP with isotropic *** with anisotropic kernels has demonstrated the most robustness,yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions,has smaller time complexity,and requires less effort in initial hyperparameter *** also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.

关键词： optimization performance acceleration

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油气田勘探数字化转型现状及展望

油气与新能源 2022年第1期34卷 67-73页

作者：任泽坤陈潇许勤中国石油天然气集团公司川庆钻探工程有限公司地质勘探开发研究院中国石油天然气股份有限公司规划总院

油气田勘探开发行业发展至今,涉及的钻井、测录井、试油等各项技术已较为成熟,可突破空间越来越小。通过现代信息技术与油气田勘探技术的跨界融合,可为油气勘探带来新的强劲发展动力,从而实现对传统勘探技术的颠覆性创新,引领油气田勘... 详细信息

油气田勘探开发行业发展至今,涉及的钻井、测录井、试油等各项技术已较为成熟,可突破空间越来越小。通过现代信息技术与油气田勘探技术的跨界融合,可为油气勘探带来新的强劲发展动力,从而实现对传统勘探技术的颠覆性创新,引领油气田勘探行业从数字化时代迈向智能化时代。在分析勘探工程数字化转型的发展基础及必要性的基础上,提出了转型的方向:全景油气田勘探物联网、智慧钻探数据生态治理、智能钻井设计、智能钻完井作业、智能技术诊断及运行故障自动处置、智能勘探装备及配套技术、井场高仿真井筒工程数字孪生、智慧地质综合研究、数字运营与决策中心建设。

关键词：油气田勘探数字化转型智能化创新驱动

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

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npj Computational Materials 2020年第1期6卷 1592-1600页

作者： zekun ren Felipe Oviedo Maung Thway Siyu I.P.Tian Yue Wang Hansong Xue Jose Dario Perea Mariya Layurova Thomas Heumueller Erik Birgersson Armin G.Aberle Christoph J.Brabec Rolf Stangl Qianxiao Li Shijing Sun Fen Lin Ian Marius Peters Tonio Buonassisi Singapore-MIT Alliance for Research and Technology SMART Singapore 138602Singapore Solar Energy Research Institute of Singapore(SERIS) National University of SingaporeSingapore 117574Singapore Massachusetts Institute of Technology CambridgeMA 02139USA Institute of Materials for Electronics and Energy Technology(i-MEET) Friedrich-Alexander University Erlangen-Nürnberg91058 ErlangenGermany Helmholtz Institute HI-ErN Forschungszentrum JülichImmerwahrstrasse 291058 ErlangenGermany National University of Singapore Singapore 119077Singapore

Process optimization of photovoltaic devices is a time-intensive,trial-and-error endeavor,which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global ***,we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide(GaAs)solar cells that identifies the root cause(s)of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box *** Bayesian network approach links a key GaAs process variable(growth temperature)to material descriptors(bulk and interface properties,e.g.,bulk lifetime,doping,and surface recombination)and device performance parameters(e.g.,cell efficiency).For this purpose,we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100×faster than numerical *** the trained surrogate model and only a small number of experimental samples,our approach reduces significantly the time-consuming intervention and characterization required by the *** a demonstration of our method,in only five metal organic chemical vapor depositions,we identify a superior growth temperature profile for the window,bulk,and back surface field layer of a GaAs solar cell,without any secondary measurements,and demonstrate a 6.5%relative AM1.5G efficiency improvement above traditional grid search methods.

关键词： knowledge network enable

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ABAP程序开发基础及开发方法

软件 2011年第6期32卷 38-41,44页

作者：任泽坤中国石油川庆钻探地质勘探开发研究院信息中心四川成都610051

本文作者对ABAP语言的概念、功能和程序模块作了简单阐述,然后进一步对它的三种开发环境以及数据层、应用层、表示层三层开发环境架构作了详细介绍。在此基础上对ABAP程序开发的基础进行了介绍,包括基本数据类型、ABAP字典、ABAP开发语... 详细信息

本文作者对ABAP语言的概念、功能和程序模块作了简单阐述,然后进一步对它的三种开发环境以及数据层、应用层、表示层三层开发环境架构作了详细介绍。在此基础上对ABAP程序开发的基础进行了介绍,包括基本数据类型、ABAP字典、ABAP开发语言的语法元素、常量和变量定义、系统定义数据、字符串数据类型申明、内表申明以及工作区定义方法,还介绍了在ABAP开发中经常使用的事件和主要的程序开发类型。作者最后结合自己的开发经验总结阐述了ABAP程序开发的六步法。

关键词： ABAP开发概述开发环境架构开发基础