A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis

Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.

Catalytic anode surface enabling in situ polymerization of gel polymer electrolyte for stable Li metal batteries

Employing quasi-solid-state gel polymer electrolyte(GPE)instead of the liquid counterpart has been regarded as a promising strategy for improving the electrochemical performance of Li metal batteries.However,the poor and uneven interfacial contact between Li metal anode and GPE could cause large interfacial resistance and electrochemical Li stripping/plating inhomogeneity,deteriorating the electrochemical performance.Herein,we proposed that the functional component of composite anode could work as the catalyst to promote the in situ polymerization reaction,and we experimentally realized the integration of polymerized-dioxolane electrolyte and Li/Li_(22)Sn_(5)/LiF composite electrode with low interfacial resistance and good stability by in situ catalyzation polymerization.Thus,the reaction kinetics and stability of metallic Li anode were significantly enhanced.As a demonstration,symmetric cell using such a GPE-Li/Li_(22)Sn_(5)/LiF integration achieved stable cycling beyond 250 cycles with small potential hysteresis of 25 mV at 1 mA·cm^(−2)and 1 mAh·cm^(−2),far outperforming the counterpart regular GPE on pure Li.Paired with LiNi0.5Co0.3Mn0.2O2,the full cell with the GPE-Li/Li_(22)Sn_(5)/LiF integration maintained 85.7%of the original capacity after 100 cycles at 0.5 C(1 C=200 mA·g^(−1)).Our research provides a promising strategy for reducing the resistance between GPE and Li metal anode,and realizes Li metal batteries with enhance electrochemical performance.

二氧化碳诱导的可逆离子液体中钯纳米颗粒的合成及应用研究

本文采用二氧化碳诱导的1,8-二氮杂二环十一碳-7-烯(DBU)和醇溶液形成的可逆离子液体为介质,通过热还原的方法制备了金属钯纳米颗粒,研究了醇的链长对粒子尺寸的影响.通过透射电子显微镜(TEM)和同步辐射小角X射线散射(SAXS)原位表征等手段对纳米颗粒的形貌和粒径等进行了表征.研究发现,在没有二氧化碳时,DBU与醇组成的溶液中合成的钯呈块状;在加入二氧化碳形成的离子液体中合成的钯纳米颗粒粒径均匀,分散性好,粒子尺寸与离子液体中醇的链长关系不大. TEM表征得到的钯纳米粒子尺寸明显大于SAXS原位表征得到的离子液体中粒子的尺寸,说明在分离过程中钯粒子发生团聚现象.此外,我们研究了该液体体系中金属钯纳米颗粒催化对甲基硝基苯和邻硝基苯甲醛加氢反应.结果表明,钯纳米颗粒对硝基的还原具有较高的催化活性,且在醛基存在时可以对硝基进行定向加氢.

Fabrication and mechanical properties of self-toughening ZrB2–SiC composites from in-situ reaction

Self-toughening ZrB2–SiC based composites are fabricated by in-situ reactive hot pressing.The effect of sintering additive content on the microstructure and mechanical properties of the composites is investigated.Microstructure observation found that the in-situ reactive hot pressing could promote the anisotropic growth of ZrB2 grains and the formation of interlocking microstructure.Such microstructure could improve the mechanical properties,especially,for the fracture toughness.The improved mechanical properties could be attributed to the self-toughening structure related to the ZrB2 platelets and the formed interlocking microstructure,which could trigger various toughening mechanisms such as grain pull-out,crack bridging,crack deflection,and crack branching,providing the main contribution to the high fracture toughness.

Machine-learning-based interatomic potentials for advanced manufacturing

This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing.Interatomic potential is essential for classical molecular dynamics.The advancements made in machine learning(ML)have enabled the development of fast interatomic potential with ab initio accuracy.The accelerated atomic simulation can greatly transform the design principle of manufacturing technology.The most widely used supervised and unsupervised ML methods are summarized and compared.Then,the emerging interatomic models based on ML are discussed:Gaussian approximation potential,spectral neighbor analysis potential,deep potential molecular dynamics,SCHNET,hierarchically interacting particle neural network,and fast learning of atomistic rare events.

Structural modulation and assembling of metal halide perovskites for solar cells and light-emitting diodes

Metal halide perovskites possess appealing optoelectronic properties and have been widely applied for solar energy harvesting and light emitting.Although perovskite solar cells(PeSCs)and perovskite light-emitting diodes(PeLEDs)have been developed rapidly in recent years,there are still no universal rules for the selection of perovskites to achieve high-performance optoelectronic devices.In this review,the working mechanisms of PeSCs and PeLEDs are first demonstrated with the discussion on the factors which determine the device performance.We then examine the optoelectronic properties of perovskites with structures modulated from 3D,2D,1D to 0D,and analyze the corresponding structure-property relationships in terms of photo-electric and electric-photo conversion processes.Based on the unique optoelectronic properties of structurally modulated perovskites,we put forward the concept of structural assembling engineering that integrate the merits of different types of perovskites within one matrix and elaborate their excellent properties for applications of both PeSCs and PeLEDs.Finally,we discuss the potential challenges and provide our perspectives on the structural assembling engineering of perovskites for future optoelectronic applications.

Insight into the nitrogen-vacancy center formation in type-Ib diamond by irradiation and annealing approach

Comprehending the microscopic formation of nitrogen vacancy(NV)centers in nitrogen-doped diamonds is crucial for enhancing the controllable preparation of NV centers and quantum applications.Irradiation followed by annealing simulations for a type-Ib diamond with a 900 ppm concentration of isolated nitrogen is conducted along different orientations and at different annealing temperatures.In these simulations,molecular dynamics(MD)with smoothly connected potential functions are implemented.MD simulations revealed the dynamic formation process of the NV center,which was subsequently verified by first-principles calculations and experiments.The results indicate that vacancies undergo one or multiple migrations by exchanging sites with neighboring atoms.There are three mechanisms for the formation of NV centers:direct irradiation-induced NV formation,irradiation with further annealing to form NV and vacancy migration(VM)during the annealing process.Furthermore,the results show that both VM and NV center formations are affected by orientations.This study clarifies the formation of NV centers across multiple scales and provides a solid foundation for the targeted preparation of NV centers.