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| 1 | Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks显示文摘X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials.We propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns.We 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 data.As a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and classified.After 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%,respectively.We 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 misclassification.Finally,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. | 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 | 2019 | npj Computational Materials2019，，1： | 15 |
| 2 | Synthesis of Alumina-Coated Natural Graphite for Highly Cycling Stability and Safety of Li-Ion Batteries显示文摘The natural graphite(NG)urdformly coated with alumina ceramics(Al2O3)was successfully synthesized through sol-gel method.The aluminum plastic film soft-packed battery prepared using Al2O3-coated NG as anode material exhibits excellent cycle performance and safety performance.The cycling retention of AN-1 is 84.95% after 200 cycles at a rate of 1 C in a potential window ranging from 3.0 to 4.35 V,which is much greater than 75.07% of NG under the same test conditions.The result of the nail penetration tests shows that the successful nail penetration rate of the NG used as anode is 0%,while that of Al2O3 coated samples AN-1 is 100%.The test results show that the Al2O3 coating could act as a solid electrolyte to suppress side reactions,improve cycle stability,and prevent a thermal runaway under mechanical abuse. | Tao Xu Chengkun Zhou Haihui Zhou Zekun Wang Jianguo Ren | 2019 | Chinese Journal of Chemistry2019，37，4： | 13 |
| 3 | Two-step machine learning enables optimized nanoparticle synthesis显示文摘In materials science,the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming.In 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 spectrum.Combining 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 conditions.Once 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 synthesis.While 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. | 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 | 2021 | npj Computational Materials2021，，1： | 3 |
| 4 | Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains显示文摘Bayesian optimization(BO)has been leveraged for guiding autonomous and high-throughput experiments in materials science.However,few have evaluated the efficiency of BO across a broad range of experimental materials domains.In this work,we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems.By 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 kernels.GP 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 selection.We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns. | 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 | 2021 | npj Computational Materials2021，，1： | 2 |
| 5 | Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics显示文摘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 optimum.Herein,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 optimization.Our 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 solvers.With 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 experimentalist.As 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. | 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 | 2020 | npj Computational Materials2020，，1： | 2 |
| 6 | A Novel Flux-weakening Control Method with Quadrature Voltage Constrain for Electrolytic Capacitorless PMSM Drives显示文摘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 system.Meanwhile, 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. | Junya Huo Dawei Ding Zekun Ren Gaolin Wang Nannan Zhao Lianghong Zhu Dianguo Xu | 2022 | CES Transactions on Electrical Machines and Systems2022，6，3： | 1 |
| 7 | SC1 inhibits the differentiation of F9 embryonic carcinoma cells induced by retinoic acid显示文摘 | Yingxiang Liu Xuexue Ren Jie Ke Yan Zhang Qing Wei Zhaopeng Shi Zhiying Ai Zekun Guo | 2018 | Acta Biochimica et Biophysica Sinica2018，50，8： | 0 |
| 8 | Identification of chemical compositions from“featureless”optical absorption spectra:Machine learning predictions and experimental validations显示文摘Rapid and accurate chemical composition identification is critically important in chemistry.While 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 research.This is due to the difficulties in identifying optical absorption peaks(i.e.,from“featureless”spectra)arose from the complexity.In 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”spectra.By 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 interpret.This 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. | Tiankai Chen Jiali Li Pengfei Cai Qiaofeng Yao Zekun Ren Yixin Zhu Saif Khan Jianping Xie Xiaonan Wang | 2023 | Nano Research2023，16，3： | 0 |
| 9 | Intelligent optimization methods of phase-modulation waveform显示文摘With the continuous improvement of radar intelligence, it is difficult for traditional countermeasures to achieve ideal results. In order to deal with complex, changeable, and unknown threat signals in the complex electromagnetic environment, a waveform intelligent optimization model based on intelligent optimization algorithm is proposed. By virtue of the universality and fast running speed of the intelligent optimization algorithm, the model can optimize the parameters used to synthesize the countermeasure waveform according to different external signals, so as to improve the countermeasure performance.Genetic algorithm(GA) and particle swarm optimization(PSO)are used to simulate the intelligent optimization of interruptedsampling and phase-modulation repeater waveform. The experimental results under different radar signal conditions show that the scheme is feasible. The performance comparison between the algorithms and some problems in the experimental results also provide a certain reference for the follow-up work. | SUN Jianwei WANG Chao SHI Qingzhan REN Wenbo YAO Zekun YUAN Naichang | 2022 | Journal of Systems Engineering and Electronics2022，33，4： | 0 |
| 10 | Author Correction:Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics显示文摘In the original version of the published Article,there was ambiguity in Eq.(1).To improve clarity,Eq.(1)has been corrected to the following. | 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 | 2020 | npj Computational Materials2020，，1： | 0 |
| 11 | Discussion on wind factor influencing the distribution of biological soil crusts on surface of sand dunes显示文摘Biological soil crusts are widely distributed in arid and semi-arid regions,whose formation and development have an important impact on the restoration process of the desert ecosystem.In order to explore the relationship between surface airflow and development characteristics of biological soil crusts,we studied surface airflow pattern and development characteristics of biological soil crusts on the fixed dune profile through field observation.Results indicate that the speed of near-surface airflow is the lowest at the foot of windward slope and the highest at the crest,showing an increasing trend from the foot to the crest.At the leeward side,although near-surface airflow increases slightly at the lower part of the slope after an initial sudden decrease at upper part of the slope,its overall trend decreases from the crest.Wind velocity variation coefficient varied at different heights over each observation site.The thickness,shear strength of biological soil crusts and percentage of fine particles at crusts layer decreased from the slope foot to the upper part,showing that biological soil crusts are less developed in high wind speed areas and well developed in low wind speed areas.It can be seen that there is a close relationship between the distribution of biological soil crusts in different parts of the dunes and changes in airflow due to geomorphologic variation. | YongSheng Wu Hasi Erdun RuiPing Yin Xin Zhang Jie Ren Jian Wang XiuMin Tian ZeKun Li HengLu Miao | 2013 | Research in Cold and Arid Regions2013，5，6： | 0 |
| 12 | A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma显示文摘Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma. | ZEKUN XIN YUDAN MA WEIQIANG SONG HAO GAO LIJUN DONG BAO ZHANG ZHILONG REN | 2023 | BIOCELL2023，47，3： | 0 |
| 13 | Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)显示文摘Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction,ecological resource management,fraud detection,and material property optimization.A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset.However,current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems,resulting in slow convergence or pigeonholing into a local minimum.In this paper,we present a Zooming Memory-Based Initialization algorithm,entitled ZoMBI,that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments.The ZoMBI algorithm demonstrates compute time speed-ups of 400×compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3×more highly optimized than those discovered by similar methods. | Alexander E.Siemenn Zekun Ren Qianxiao Li Tonio Buonassisi | 2023 | npj Computational Materials2023，，1： | 0 |
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