Constrained crystals deep convolutional generative adversarial network for the inverse design of crystal structures

Autonomous materials discovery with desired properties is one of the ultimate goals for materials science,and the current studies have been focusing mostly on high-throughput screening based on density functional theory calculations and forward modeling of physical properties using machine learning.Applying the deep learning techniques,we have developed a generative model,which can predict distinct stable crystal structures by optimizing the formation energy in the latent space.It is demonstrated that the optimization of physical properties can be integrated into the generative model as on-top screening or backward propagator,both with their own advantages.Applying the generative models on the binary Bi-Se system reveals that distinct crystal structures can be obtained covering the whole composition range,and the phases on the convex hull can be reproduced after the generated structures are fully relaxed to the equilibrium.The method can be extended to multicomponent systems for multi-objective optimization,which paves the way to achieve the inverse design of materials with optimal properties.

Multifunctional antiperovskites driven by strong magnetostructural coupling

Based on density functional theory calculations,we elucidated the origin of multifunctional properties for cubic antiperovskites with noncollinear magnetic ground states,which can be attributed to strong isotropic and anisotropic magnetostructural coupling.Of 54 stable magnetic antiperovskites M_(3)XZ(M=Cr,Mn,Fe,Co,and Ni;X=selected elements from Li to Bi except for noble gases and 4f rare-earth metals;and Z=C and N),14 are found to exhibit the Γ_(4g)/Γ_(5g)(i.e.,characterized by irreducible representations)antiferromagnetic magnetic configurations driven by frustrated exchange coupling and strong magnetocrystalline anisotropy.Using the magnetic deformation as an effective proxy,the isotropic magnetostructural coupling is characterized,and it is observed that the paramagnetic state is critical to understand the experimentally observed negative thermal expansion and to predict the magnetocaloric performance.Moreover,the piezomagnetic and piezospintronic effects induced by biaxial strain are investigated.It is revealed that there is not a strong correlation between the induced magnetization and anomalous Hall conductivities by the imposed strain.Interestingly,the anomalous Hall/Nernst conductivities can be significantly tailored by the applied strain due to the fine-tuning of the Weyl points energies,leading to promising spintronic applications.

Thermodynamical and topological properties of metastable Fe_(3)Sn

The Fe–Sn-based kagome compounds attract intensive attention due to its attractive topological transport and rich magnetic properties.Combining experimental data,first-principles calculations,and Calphad assessment,thermodynamic and topological transport properties of the Fe–Sn system were investigated.Density functional theory(DFT)calculations were performed to evaluate the intermetallics’finite-temperature heat capacity(C_(p)).A consistent thermodynamic assessment of the Fe–Sn phase diagram was achieved by using the experimental and DFT results,together with all available data from previous publications.Here,we report that the metastable phase Fe_(3)Sn was introduced into the current metastable phase diagram,and corrected phase locations of Fe_(5)Sn_(3)and Fe_(3)Sn_(2)under the newly measured corrected temperature ranges.Furthermore,the anomalous Hall conductivity and anomalous Nernst conductivity of Fe_(3)Sn were calculated,with magnetization directions and doping considered as perturbations to tune such transport properties.It was observed that the enhanced anomalous Hall and Nernst conductivities originate from the combination of nodal lines and small gap areas that can be tuned by doping Mn at Fe sites and varying magnetization direction.