In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of insights.Automated techniques require human intervention,and lack the perf...In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of insights.Automated techniques require human intervention,and lack the performance and adaptability required for material exploration.Given the critical need for high-throughput automated XRD pattern analysis,we present a generalized deep learning model to classify a diverse set of materials’crystal systems and space groups.In our approach,we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties.We also employ an expedited learning technique to refine our model’s expertise to experimental conditions.In addition,we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making.We evaluate our models using experimental data,materials unseen in training,and altered cubic crystals,where we observe state-of-the-art performance and even greater advances in space group classification.展开更多
The high permeability and strong selectivity of nanoporous silicon nitride(NPN)membranes make them attractive in a broad range of applications.Despite their growing use,the strength of NPN membranes needs to be improv...The high permeability and strong selectivity of nanoporous silicon nitride(NPN)membranes make them attractive in a broad range of applications.Despite their growing use,the strength of NPN membranes needs to be improved for further extending their biomedical applications.In this work,we implement a deep learning framework to design NPN membranes with improved or prescribed strength values.We examine the predictions of our framework using physics-based simulations.Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures,but also can design NPN membranes with prescribed or improved strength.Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design,lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.展开更多
In the Acknowledgements section of this article the grant number relating to National Science Foundation given for Chenliang Xu and Niaz Abdolrahim was incorrectly given as NSF 2202124 and should have been DMR-2202124...In the Acknowledgements section of this article the grant number relating to National Science Foundation given for Chenliang Xu and Niaz Abdolrahim was incorrectly given as NSF 2202124 and should have been DMR-2202124.The original article has been corrected.展开更多
基金J.S.,S.L.,C.X.,and N.A.were supported by the National Nuclear Security Administration via grant NA0004078C.X.and N.A.were also supported by the National Science Foundation via grant NSF 2202124N.A.was also supported by the U.S.Department of Energy(DOE),Office of Science(SC),and Fusion Energy Sciences,under Award Number DE-SC0020340.
文摘In current in situ X-ray diffraction(XRD)techniques,data generation surpasses human analytical capabilities,potentially leading to the loss of insights.Automated techniques require human intervention,and lack the performance and adaptability required for material exploration.Given the critical need for high-throughput automated XRD pattern analysis,we present a generalized deep learning model to classify a diverse set of materials’crystal systems and space groups.In our approach,we generate training data with a holistic representation of patterns that emerge from varying experimental conditions and crystal properties.We also employ an expedited learning technique to refine our model’s expertise to experimental conditions.In addition,we optimize model architecture to elicit classification based on Bragg’s Law and use evaluation data to interpret our model’s decision-making.We evaluate our models using experimental data,materials unseen in training,and altered cubic crystals,where we observe state-of-the-art performance and even greater advances in space group classification.
文摘The high permeability and strong selectivity of nanoporous silicon nitride(NPN)membranes make them attractive in a broad range of applications.Despite their growing use,the strength of NPN membranes needs to be improved for further extending their biomedical applications.In this work,we implement a deep learning framework to design NPN membranes with improved or prescribed strength values.We examine the predictions of our framework using physics-based simulations.Our results confirm that the proposed framework is not only able to predict the strength of NPN membranes with a wide range of microstructures,but also can design NPN membranes with prescribed or improved strength.Our simulations further demonstrate that the microstructural heterogeneity that our framework suggests for the optimized design,lowers the stress concentration around the pores and leads to the strength improvement of NPN membranes as compared to conventional membranes with homogenous microstructures.
文摘In the Acknowledgements section of this article the grant number relating to National Science Foundation given for Chenliang Xu and Niaz Abdolrahim was incorrectly given as NSF 2202124 and should have been DMR-2202124.The original article has been corrected.
