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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 | 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 |
| 3 | 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 |
| 4 | Albumin-induced premature senescence in human renal proximal tubular cells and its relationship with intercellular fibrosis显示文摘The presence of senescent cells is associated with renal fibrosis.This study aims to investigate the effect of albumininduced premature senescence on tubulointerstitial fibrosis and its possible mechanism in vitro.Different concentrations of bovine serum albumim(BSA)with or without si-p21 are used to stimulate HK-2 cells for 72 h,and SA-β-gal activity,senescence-associated secretory phenotypes(SASPs),LaminB1 are used as markers of senescence.Immunofluorescence staining is performed to characterize the G2/M phase arrest between the control and BSA groups.Alterations in the DNA damage markerγ-H2AX,fibrogenesis,and associated proteins at the G2/M phase,such as p21,p-CDC25C and p-CDK1,are evaluated.Compared with those in the control group,the SA-β-gal activity,SASP,andγ-H2AX levels are increased in the BSA group,while the level of LaminB1 is decreased.Meanwhile,HK-2 cells blocked at the G2/M phase are significantly increased under the stimulation of BSA,and the levels of p21,p-CDC25C and p-CDK1,as well as fibrogenesis are also increased.When p21 expression is inhibited,the levels of p-CDC25C and p-CDK1 are decreased and the G2/M phase arrest is improved,which decreases the production of fibrogenesis.In conclusion,BSA induces renal tubular epithelial cell premature senescence,which regulates the G2/M phase through the CDC25C/CDK1 pathway,leading to tubulointerstitial fibrosis. | Wen Lu Shijing Ren Wenhui Dong Xiaomin Li Zongji Zheng Yijie Jia Yaoming Xue | 2022 | Acta Biochimica et Biophysica Sinica2022，54，7： | 1 |
| 5 | 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 |
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