High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.

ZJ116A卷接机单支重量加工精度的优化研究

作为单支重量加工精度的重要指标,烟支的重量标准偏差反映了烟支内在品质的稳定性。前期研究找到了影响卷烟重量标准偏差的6项关键因子。该文的DOE试验选用确定性筛选设计(Definitive Screening Design,DSD)方法,通过合理安排试验的运行顺序和条件,找到了关键因子间的关系和最佳搭配。一次试验实现了筛选重要因子、描述基本特性以及找到最优方案等目标。经过ZJ116A试生产验证,重量标准偏差均值为18.86mg,重量标准偏差合格率为95%,取得了满意效果。

Modeling, analysis, and optimization of dimensional accuracy of FDM-fabricated parts using definitive screening design and deep learning feedforward artificial neural network

Additive manufacturing(AM)technologies such as fused deposition modeling(FDM)rely on the quality of manufactured products and the process capability.Currently,the dimensional accuracy and stability of any AM process is essential for ensuring that customer specifications are satisfied at the highest standard,and variations are controlled without significantly affecting the functioning of processes,machines,and product structures.This study aims to investigate the effects of FDM fabrication conditions on the dimensional accuracy of cylindrical parts.In this study,a new class of experimental design techniques for integrated second-order definitive screening design(DSD)and an artificial neural network(ANN)are proposed for designing experiments to evaluate and predict the effects of six important operating variables.By determining the optimum fabrication conditions to obtain better dimensional accuracies for cylindrical parts,the time consumption and number of complex experiments are reduced considerably in this study.The optimum fabrication conditions generated through a second-order DSD are verified with experimental measurements.The results indicate that the slice thickness,part print direction,and number of perimeters significantly affect the percentage of length difference,whereas the percentage of diameter difference is significantly affected by the raster-to-raster air gap,bead width,number of perimeters,and part print direction.Furthermore,the results demonstrate that a second-order DSD integrated with an ANN is a more attractive and promising methodology for AM applications.

Advances in data-assisted high-throughput computations for material design

Extensive trial and error in the variable space is the main cause of low efficiency and high cost in material development.The experimental tasks can be reduced significantly in the case that the variable space is narrowed down by reliable computer simulations.Because of their numerous variables in material design,however,the variable space is still too large to be accessed thoroughly even with a computational approach.High-throughput computations(HTC)make it possible to complete a material screening in a large space by replacing the conventionally manual and sequential operations with automatic,robust,and concurrent streamlines.The efficiency of HTC,which is one of the pillars of materials genome engineering,has been verified in many studies,but its applications are still limited by demanding computational costs.Introduction of data mining and artificial intelligence into HTC has become an effective approach to solve the problem.In the past years,many studies have focused on the development and application of HTC and data combined approaches,which is considered as a new paradigm in computational materials science.This review focuses on the main advances in the field of data-assisted HTC for material research and development and provides our outlook on its future development.

Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing

Functional materials are widely used in chemical industry in order to reduce the process cost while simultaneously increase the product quality.Considering their significant effects,systematic methods for the optimal selection and design of materials are essential.The conventional synthesis-and-test method for materials development is inefficient and costly.Additionally,the performance of the resulting materials is usually limited by the designer’s expertise.During the past few decades,computational methods have been significantly developed and they now become a very important tool for the optimal design of functional materials for various chemical processes.This article selectively focuses on two important process functional materials,namely heterogeneous catalyst and gas separation agent.Theoretical methods and representative works for computational screening and design of these materials are reviewed.