We investigate the phenomena of spontaneous symmetry breaking for φ^4 model on a square lattice in the parameter space by using the potential importance samplingmethod, which was proposed by Milchev, Heermann, and Bi...We investigate the phenomena of spontaneous symmetry breaking for φ^4 model on a square lattice in the parameter space by using the potential importance samplingmethod, which was proposed by Milchev, Heermann, and Binder [J. Star. Phys. 44 （1986） 749]. The critical values of the parameters allow us to determine the phase diagram of the model. At the same time, some relevant quantifies such as susceptibility and specific heat are also obtained.展开更多
Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing m...Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing machine learning interatomic potentials(MLIPs)often fall short in adequately describing these defects,as their large characteristic scales exceed the computational limits of firstprinciples calculations.To address this challenge,wepresent acomputational frameworkcombining a defect genome constructed via empirical interatomic potential-guided sampling,with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations.The effectiveness of this approach was validated through simulations of nanoindentation,tensile deformation,and fracture in BCC tungsten.This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.展开更多
Materials design using Bayesian optimization(BO)typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions.However,materials often possess good properties at specific val...Materials design using Bayesian optimization(BO)typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions.However,materials often possess good properties at specific values or show effective response under certain conditions.We propose a target-oriented BO to efficiently suggest materials with target-specific properties.The method samples potential candidates by allowing their properties to approach the target value from either above or below,minimizing experimental iterations.We compare the performance of target-oriented BOwith that of otherBOmethods on synthetic functions and materials databases.The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target,especially when the training dataset is small.Wefurther employ the method to discover a thermally-responsive shape memory alloy Ti_(0.20)Ni_(0.36)Cu_(0.12)Hf_(0.24)Zr_(0.08)with a transformation temperature difference of only 2.66℃(0.58%of the range)from the target temperature in 3 experimental iterations.Our method provides a solution tailored for optimizing target-specific properties,facilitating the accelerated development of materials with predefined properties.展开更多
文摘We investigate the phenomena of spontaneous symmetry breaking for φ^4 model on a square lattice in the parameter space by using the potential importance samplingmethod, which was proposed by Milchev, Heermann, and Binder [J. Star. Phys. 44 （1986） 749]. The critical values of the parameters allow us to determine the phase diagram of the model. At the same time, some relevant quantifies such as susceptibility and specific heat are also obtained.
基金sponsored by Nederlandse Organisatie voor WetenschappelijkOnderzoek(The Netherlands Organization for Scientific Research,NWO)domain Science for the use of supercomputer facilities.
文摘Extended defects such as dislocation networks and general grain boundaries are ubiquitous in metals,and accurate modeling these extensive defects is crucial to elucidate their deformation mechanisms.However,existing machine learning interatomic potentials(MLIPs)often fall short in adequately describing these defects,as their large characteristic scales exceed the computational limits of firstprinciples calculations.To address this challenge,wepresent acomputational frameworkcombining a defect genome constructed via empirical interatomic potential-guided sampling,with an automated reconstruction technique that enables accurate first-principles modeling of general defects by converting atomic clusters into periodic configurations.The effectiveness of this approach was validated through simulations of nanoindentation,tensile deformation,and fracture in BCC tungsten.This framework enhances the modeling accuracy of extended defects in crystalline materials and provides a robust foundation for advancing MLIP development by leveraging defect genomes strategically.
基金the support of the National Key Research and Development Program of China(2021YFB3802102)National Natural Science Foundation of China(Grant Nos.52303297,52350710205)China Postdoctoral Science Foundation(2022TQ0201,2023M742196).
文摘Materials design using Bayesian optimization(BO)typically focuses on optimizing materials properties by estimating the maxima/minima of unknown functions.However,materials often possess good properties at specific values or show effective response under certain conditions.We propose a target-oriented BO to efficiently suggest materials with target-specific properties.The method samples potential candidates by allowing their properties to approach the target value from either above or below,minimizing experimental iterations.We compare the performance of target-oriented BOwith that of otherBOmethods on synthetic functions and materials databases.The average results from hundreds of repeated trials demonstrate target-oriented BO requires fewer experimental iterations to reach the same target,especially when the training dataset is small.Wefurther employ the method to discover a thermally-responsive shape memory alloy Ti_(0.20)Ni_(0.36)Cu_(0.12)Hf_(0.24)Zr_(0.08)with a transformation temperature difference of only 2.66℃(0.58%of the range)from the target temperature in 3 experimental iterations.Our method provides a solution tailored for optimizing target-specific properties,facilitating the accelerated development of materials with predefined properties.
