Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achi...Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time points.However,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing burden.To address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency reconstruction.This algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used Wiener-SIM.Critically,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.展开更多
High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. T...High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work,we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential(DLP) for high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C is fitted with the prediction error in energy and force being 9.4 me V/atom and 217 me V/?, respectively. The reliability and generality of the DLP are affirmed,since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC(TM = Ti, Zr, Hf, Nb and Ta). Lattice constants(increase from 4.5707 ? to 4.6727 ?), thermal expansion coefficients(increase from 7.85×10-6 K^(-1) to 10.58×10-6 K^(-1)), phonon thermal conductivities(decrease from 2.02 W·m-1·K^(-1) to 0.95 W·m-1·K^(-1)), and elastic properties of high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C in temperature ranging from 0°C to 2400°C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods.展开更多
High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical...High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit now.In this work,variations of thermal and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) with respect to temperature were predicted by molecular dynamics simulations.Firstly,a deep learning potential for Ti-Zr-Hf-Nb-Ta-B diboride system was fitted with its prediction error in energy and force respectively being 9.2 meV/atom and 208 meV/A,in comparison with first-principles calculations.Then,temperature dependent lattice constants,anisotropic thermal expansions,anisotropic phonon thermal conductivities,and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) from 0℃to 2400℃were evaluated,where the predicted room temperature values agree well with experimental measurements.In addition,intrinsic lattice distortions of(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) were analyzed by displacements of atoms from their ideal positions,which are in an order of 10^(-3) A and one order of magnitude smaller than those in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))C.It indicates that lattice distortions in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) is not so severe as expected.With the new paradigm of machine learning potential,deep insight into high entropy materials can be achieved in the future,since the chemical and structural complexly in high entropy materials can be well handled by machine learning potential.展开更多
The compressive stress-strain relationships of 6061A1 alloy over wide temperatures and strain rates are investigated. The dynamic impact experiments are performed using an im- proved high temperature split Hopkinson p...The compressive stress-strain relationships of 6061A1 alloy over wide temperatures and strain rates are investigated. The dynamic impact experiments are performed using an im- proved high temperature split Hopkinson pressure bar apparatus. The experimental results are compared with those obtained by the modified Johnson-Cook constitutive model. It is found that the dynamic mechanical behavior depends sensitively on temperature under relatively low strain rates or on strain rate at relatively high temperatures. The good agreement indicates that it is valid to adopt the parameter identification method and the constitutive model to describe and predict the mechanical response of materials.展开更多
基金supported by the National Natural Science Foundation of China (NSFC) (Nos. 62005208, 62135003, and 61905189)Innovation Capability Support Program of Shaanxi (No. 2021TD-57)+1 种基金China Postdoctoral Science Foundation (Nos. 2020M673365 and 2019M663656)National Institutes of Health Grant GM100156 to PRB
文摘Super-resolution structured illumination microscopy(SR-SIM)is an outstanding method for visualizing the subcellular dynamics in living cells.To date,by using elaborately designed systems and algorithms,SR-SIM can achieve rapid,optically sectioned,SR observation with hundreds to thousands of time points.However,real-time observation is still out of reach for most SIM setups as conventional algorithms for image reconstruction involve a heavy computing burden.To address this limitation,an accelerated reconstruction algorithm was developed by implementing a simplified workflow for SR-SIM,termed joint space and frequency reconstruction.This algorithm results in an 80-fold improvement in reconstruction speed relative to the widely used Wiener-SIM.Critically,the increased processing speed does not come at the expense of spatial resolution or sectioning capability,as demonstrated by live imaging of microtubule dynamics and mitochondrial tubulation.
基金supported financially by the National Natural Science Foundation of China(Nos.51672064 and No.U1435206)。
文摘High entropy materials(HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials. Though a large number of HEMs have emerged, there is still in lack of theoretical predictions and simulations on HEMs, which is probably caused by the chemical complexity of HEMs. In this work,we demonstrate that the machine learning potentials developed in recent years can overcome the complexity of HEMs, and serve as powerful theoretical tools to simulate HEMs. A deep learning potential(DLP) for high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C is fitted with the prediction error in energy and force being 9.4 me V/atom and 217 me V/?, respectively. The reliability and generality of the DLP are affirmed,since it can accurately predict lattice parameters and elastic constants of mono-phase carbides TMC(TM = Ti, Zr, Hf, Nb and Ta). Lattice constants(increase from 4.5707 ? to 4.6727 ?), thermal expansion coefficients(increase from 7.85×10-6 K^(-1) to 10.58×10-6 K^(-1)), phonon thermal conductivities(decrease from 2.02 W·m-1·K^(-1) to 0.95 W·m-1·K^(-1)), and elastic properties of high entropy(Zr(0.2) Hf(0.2) Ti(0.2) Nb(0.2) Ta(0.2))C in temperature ranging from 0°C to 2400°C are predicted by molecular dynamics simulations. The predicted room temperature properties agree well with experimental measurements, indicating the high accuracy of the DLP. With introducing of machine learning potentials, many problems that are intractable by traditional methods can be handled now. It is hopeful that deep insight into HEMs can be obtained in the future by such powerful methods.
基金supported by Natural Sciences Foundation of China under Grant No.51972089 and No.51672064。
文摘High entropy diborides are new categories of ultra-high temperature ceramics,which are believed promising candidates for applications in hypersonic vehicles.However,knowledge on high temperature thermal and mechanical properties of high entropy diborides is still lacking unit now.In this work,variations of thermal and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) with respect to temperature were predicted by molecular dynamics simulations.Firstly,a deep learning potential for Ti-Zr-Hf-Nb-Ta-B diboride system was fitted with its prediction error in energy and force respectively being 9.2 meV/atom and 208 meV/A,in comparison with first-principles calculations.Then,temperature dependent lattice constants,anisotropic thermal expansions,anisotropic phonon thermal conductivities,and elastic properties of high entropy(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) from 0℃to 2400℃were evaluated,where the predicted room temperature values agree well with experimental measurements.In addition,intrinsic lattice distortions of(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) were analyzed by displacements of atoms from their ideal positions,which are in an order of 10^(-3) A and one order of magnitude smaller than those in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))C.It indicates that lattice distortions in(Ti_(0.2)Zr_(0.2)Hf_(0.2)Nb_(0.2)Ta_(0.2))B_(2) is not so severe as expected.With the new paradigm of machine learning potential,deep insight into high entropy materials can be achieved in the future,since the chemical and structural complexly in high entropy materials can be well handled by machine learning potential.
基金Project supported by the State 973 Program of China (No. 2013CB03570)the National Natural Science Foundation of China (Nos. 11272259, 11002104, 10902090 and 11021202)
文摘The compressive stress-strain relationships of 6061A1 alloy over wide temperatures and strain rates are investigated. The dynamic impact experiments are performed using an im- proved high temperature split Hopkinson pressure bar apparatus. The experimental results are compared with those obtained by the modified Johnson-Cook constitutive model. It is found that the dynamic mechanical behavior depends sensitively on temperature under relatively low strain rates or on strain rate at relatively high temperatures. The good agreement indicates that it is valid to adopt the parameter identification method and the constitutive model to describe and predict the mechanical response of materials.
