The Influence of the Pluralism of Chinese Language and Literature on the Tradition of Literary Criticism

The current problems facing literary criticism are not only the influence of the external environment,but also the understanding of the traditional Chinese culture from the perspective of“globalization”and“diversification”of literature.It is also a guide for related literary criticism.This paper first discusses the pluralism of literary criticism,gives an overview of literary criticism,analyzes the problems of literary criticism in Chinese literature in our country,and finally propoeses relevant suggestions for literary criticism under pluralism.

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

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.

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

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.

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

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.

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

While machine learning(ML)in experimental research has demonstrated impressive predictive capabilities,extracting fungible knowledge representations from experimental data remains an elusive task.In this manuscript,we use ML to infer the underlying differential equation(DE)from experimental data of degrading organic-inorganic methylammonium lead iodide(MAPI)perovskite thin films under environmental stressors(elevated temperature,humidity,and light).Using a sparse regression algorithm,we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85℃is described minimally by a second-order polynomial.This DE corresponds to the Verhulst logistic function,which describes reaction kinetics analogous to self-propagating reactions.We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied.Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.

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

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.