The kinetic properties of Mg alloy melts are crucial for determining the forming quality of castings,as they directly affect crystal nucleation and dendritic growth.However,accurately assessing the kinetic properties ...The kinetic properties of Mg alloy melts are crucial for determining the forming quality of castings,as they directly affect crystal nucleation and dendritic growth.However,accurately assessing the kinetic properties of molten Mg alloys remains challenging due to the difficulties in experimentally character-izing the high-temperature melts.Herein,we propose that molecular dynamics(MD)simulations driven by deep learning based interatomic potentials(DPs),referred to as DPMD,are a promising strategy to tackle this challenge.We develop MgAl-DP,MgSi-DP,MgCa-DP,and MgZn-DP to assess the kinetic prop-erties of Mg-Al,Mg-Si,Mg-Ca,and Mg-Zn alloy melts.The reliability of our DPs is rigorously evaluated by comparing the DPMD results with those from ab initio MD(AIMD)simulations,as well as available ex-perimental results.Our theoretically evaluated viscosity of Mg-Al melts shows excellent agreement with experimental results over a wide temperature range.Additionally,we found that the solute elements Ca and Zn exhibit sluggish kinetics in the studied melts,which supporting the promising glass-forming abil-ity of the Mg-Zn-Ca alloy system.The computational efficiency of DPMD simulations is several orders of magnitude higher than that of AIMD simulations,while maintaining ab initio-level accuracy.This makes DPMD a highly feasible protocol for building a comprehensive and reliable database of kinetic properties of Mg alloy melts.展开更多
Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lack...Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lacking.Herein,we developed a deep learning-based interatomic potential to perform molecu-lar dynamics(MD)simulations to study the martensitic phase transformation across a range of carbon(C)concentrations.The results revealed that an increased C concentration leads to a suppressed phase boundary movement and a decelerated phase transformation rate.To overcome the timescale limitations inherent in MD simulations,metadynamics sampling was employed to accelerate the simulations of C dif-fusion.We found that C atoms tend to cluster at distances equivalent to the lattice parameter of Fe with the same sublattice occupation,leading to local lattice tetragonality.Such C-ordered structures effectively inhibit dislocation movement and enhance strength.The stress field induced by dislocations facilitates a higher degree of ordering,and the formation of C-ordered structures was identified as a potentially cru-cial strengthening mechanism for martensitic steels.The consistency between our simulation results and reported experimental observations underscores the effectiveness of the developed DP model in simu-lating martensitic phase transformation in Fe-C alloys,providing detailed insights into the mechanisms underlying this process.This work not only advances the understanding of martensitic phase transforma-tions in Fe-C alloys but also establishes a powerful computational framework for designing steels with optimized mechanical properties through the precise control of carbon ordering and dislocation behavior.展开更多
Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of dis...Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).展开更多
Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed...Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed so far.Here,we first develop a deep learning potential for B_(12)P_(2) based on quantum mechanical calculations.Then the isotropic lattice thermal conductivity(LTC)of crystalline B_(12)P_(2) is predicted to be 39.70±4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential.The LTC exhibits the relationship ofκL~1/T in the temperature range of 300~1500 K.More important,a twin boundary strategy is proposed to reduce the LTC of B_(12)P_(2).In nanotwinned B_(12)P_(2),the phonon transport in all directions is significantly suppressed by twin boundaries(TBs)with the isotropic LTC of 15.85±2.70 W/m⋅K,especially in the direction normal to the TB plane.The decrease of vibrational density of states and phonon participation ratio due to TBs’phonon scattering is the main reason of the low LTC in nanotwinned B_(12)P_(2).In addition,the elastic moduli(B and G)of B_(12)P_(2) crystal decrease by less than 7%after inducing TBs,which suggests that the mechanical properties are not significantly affected by TBs.Overall,this work enriches our understanding of the thermal properties of B_(12)P_(2) and offers a promising approach,i.e.,introducing TBs,to design high-performance thermoelectric materials.展开更多
Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervent...Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses.展开更多
基金Financial support from the National Natural Science Founda-tion of China(Nos.52222409 and52074132)the National Key Research and Development Program(No.2022YFE0122000)+1 种基金Partial financial support comes from the Science and Technology Development Program of Jilin Province(No.20210301025GX)the Fundamental Research Funds for the Central Universities,JLU.
文摘The kinetic properties of Mg alloy melts are crucial for determining the forming quality of castings,as they directly affect crystal nucleation and dendritic growth.However,accurately assessing the kinetic properties of molten Mg alloys remains challenging due to the difficulties in experimentally character-izing the high-temperature melts.Herein,we propose that molecular dynamics(MD)simulations driven by deep learning based interatomic potentials(DPs),referred to as DPMD,are a promising strategy to tackle this challenge.We develop MgAl-DP,MgSi-DP,MgCa-DP,and MgZn-DP to assess the kinetic prop-erties of Mg-Al,Mg-Si,Mg-Ca,and Mg-Zn alloy melts.The reliability of our DPs is rigorously evaluated by comparing the DPMD results with those from ab initio MD(AIMD)simulations,as well as available ex-perimental results.Our theoretically evaluated viscosity of Mg-Al melts shows excellent agreement with experimental results over a wide temperature range.Additionally,we found that the solute elements Ca and Zn exhibit sluggish kinetics in the studied melts,which supporting the promising glass-forming abil-ity of the Mg-Zn-Ca alloy system.The computational efficiency of DPMD simulations is several orders of magnitude higher than that of AIMD simulations,while maintaining ab initio-level accuracy.This makes DPMD a highly feasible protocol for building a comprehensive and reliable database of kinetic properties of Mg alloy melts.
基金the National Key Research and Devel-opment Program of China(No.2022YFB3709000)the National Natural Science Foundation of China(Nos.52101019,52122408,52071023,52474397)+1 种基金support from the Fundamental Research Funds for the Central Universities(University of Science and Technology Beijing,No.FRF-TP-2021-04C1,and 06500135)supported by USTB MatCom of Beijing Advanced Innovation Center for Materials Genome Engineering.
文摘Martensitic transformation plays a pivotal role in strengthening and hardening of steels,yet an accu-rate interatomic potential for a comprehensive description of the martensitic phase formation in Fe-C alloys is lacking.Herein,we developed a deep learning-based interatomic potential to perform molecu-lar dynamics(MD)simulations to study the martensitic phase transformation across a range of carbon(C)concentrations.The results revealed that an increased C concentration leads to a suppressed phase boundary movement and a decelerated phase transformation rate.To overcome the timescale limitations inherent in MD simulations,metadynamics sampling was employed to accelerate the simulations of C dif-fusion.We found that C atoms tend to cluster at distances equivalent to the lattice parameter of Fe with the same sublattice occupation,leading to local lattice tetragonality.Such C-ordered structures effectively inhibit dislocation movement and enhance strength.The stress field induced by dislocations facilitates a higher degree of ordering,and the formation of C-ordered structures was identified as a potentially cru-cial strengthening mechanism for martensitic steels.The consistency between our simulation results and reported experimental observations underscores the effectiveness of the developed DP model in simu-lating martensitic phase transformation in Fe-C alloys,providing detailed insights into the mechanisms underlying this process.This work not only advances the understanding of martensitic phase transforma-tions in Fe-C alloys but also establishes a powerful computational framework for designing steels with optimized mechanical properties through the precise control of carbon ordering and dislocation behavior.
基金Project supported by the Joint Fund of the National Natural Science Foundation of China–“Ye Qisun”Science Fund(Grant No.U2341251)。
文摘Zirconium hydride(ZrH_(2)) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_(2).Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. Machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_(2) system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_(2) system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_(2).
文摘Boron subphosphide(B_(12)P_(2))is a promising high temperature thermoelectric material due to its good thermal stability,and chemical inertness.However,the thermal properties of B_(12)P_(2) have not been well revealed so far.Here,we first develop a deep learning potential for B_(12)P_(2) based on quantum mechanical calculations.Then the isotropic lattice thermal conductivity(LTC)of crystalline B_(12)P_(2) is predicted to be 39.70±4.38 W/m⋅K from molecular dynamics simulations using this deep learning potential.The LTC exhibits the relationship ofκL~1/T in the temperature range of 300~1500 K.More important,a twin boundary strategy is proposed to reduce the LTC of B_(12)P_(2).In nanotwinned B_(12)P_(2),the phonon transport in all directions is significantly suppressed by twin boundaries(TBs)with the isotropic LTC of 15.85±2.70 W/m⋅K,especially in the direction normal to the TB plane.The decrease of vibrational density of states and phonon participation ratio due to TBs’phonon scattering is the main reason of the low LTC in nanotwinned B_(12)P_(2).In addition,the elastic moduli(B and G)of B_(12)P_(2) crystal decrease by less than 7%after inducing TBs,which suggests that the mechanical properties are not significantly affected by TBs.Overall,this work enriches our understanding of the thermal properties of B_(12)P_(2) and offers a promising approach,i.e.,introducing TBs,to design high-performance thermoelectric materials.
基金National Thousand Young Talents Program of China,and the National Natural Science Foundation of China(NSFC 52201180).
文摘Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses.
