The highest melting point material:Searched by Bayesian global optimization with deep potential molecular dynamics

The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic vehicles.However,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly difficult.In the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure carbides.The solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting point.The highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary system.Dominating mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less stable.The improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.

Theoretical prediction on the local structure and transport properties of molten alkali chlorides by deep potentials

In this work,the local structure and transport properties of three typical alkali chlorides(LiCl,NaCl,and KCl)were investigated by our newly trained deep potentials(DPs).We extracted datasets from ab initio molecular dynamics(AIMD)calculations and used these to train and validate the DPs.Large-scale and long-time molecular dynamics simulations were performed over a wider range of temperatures than AIMD to confirm the reliability and generality of the DPs.We demonstrated that the generated DPs can serve as a powerful tool for simulating alkali chlorides;the DPs also provide results with accuracy that is comparable to that of AIMD and efficiency that is similar to that of empirical potentials.The partial radial distribution functions and angle distribution functions predicted using the DPs are in close agreement with those derived from AIMD.The estimated densities,self-diffusion coefficients,shear viscosities,and electrical conductivities also matched well with the AIMD and experimental data.This work provides confidence that DPs can be used to explore other systems,including mixtures of chlorides or entirely different salts.

Deep potentials for materials science

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;i.e.machine learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)method.In this review,we provide an introduction to DP methods in computational materials science.The theory underlying the DP method is presented along with a step-by-step introduction to their development and use.We also review materials applications of DPs in a wide range of materials systems.The DP Library provides a platform for the development of DPs and a database of extant DPs.We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

The Coherence of a Dipolar Condensate in a Harmonic Potential Superimposed to a Deep Lattice

Within the mean-field model, the coherent matter waves of a dipolar condensate in a harmonic potentiM super- imposed to a deep lattice are investigated by the variational princip]e. It is shown that, in a harmonic potential superimposed to a deep lattice, it is possible to control the decoherence of Bloch oscillations due to the fact that the on-site and the inter-site dipolar interactions can not only damp out Bloch oscillations but also maintain long-lived Bloch oscillations under the certain condition. In particular, long-lived Bloch oscillations of dipolar condensate can be realized when the dipolar interaction, the contact interaction, the frequency of the harmonic potentiM and initial width of the wave packet satisfy an analytical condition. Thus the decoherence of Bloch os- cillation can be controlled by adjusting the dipolar interaction, the contact interaction, the frequency of harmonic potentiM and the initial width of the wave packet.

Nanotwinning induced decreased lattice thermal conductivity of high temperature thermoelectric boron subphosphide (B12P2) from deep learning potential simulations

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.

Fragility crossover mediated by covalent-like electronic interactions in metallic liquids

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.