Negative Poisson's Ratios of Layered Materials by First-Principles High-Throughput Calculations

Auxetic two-dimensional(2D)materials,known from their negative Poisson's ratios(NPRs),exhibit the unique property of expanding(contracting)longitudinally while being laterally stretched(compressed),contrary to typical materials.These materials offer improved mechanical characteristics and hold great potential for applications in nanoscale devices such as sensors,electronic skins,and tissue engineering.Despite their promising attributes,the availability of 2D materials with NPRs is limited,as most 2D layered materials possess positive Poisson's ratios.In this study,we employ first-principles high-throughput calculations to systematically explore Poisson's ratios of 40 commonly used 2D monolayer materials,along with various bilayer structures.Our investigation reveals that BP,GeS and GeSe exhibit out-of-plane NPRs due to their hinge-like puckered structures.For 1T-type transition metal dichalcogenides such as M X_(2)(M=Mo,W;X=S,Se,Te)and transition metal selenides/halides the auxetic behavior stems from a combination of geometric and electronic structural factors.Notably,our findings unveil V_(2)O_(5) as a novel material with out-of-plane NPR.This behavior arises primarily from the outward movement of the outermost oxygen atoms triggered by the relaxation of strain energy under uniaxial tensile strain along one of the in-plane directions.Furthermore,our computations demonstrate that Poisson's ratio can be tuned by varying the bilayer structure with distinct stacking modes attributed to interlayer coupling disparities.These results not only furnish valuable insights into designing 2D materials with a controllable NPR but also introduce V_(2)O_(5) as an exciting addition to the realm of auxetic 2D materials,holding promise for diverse nanoscale applications.

Universal materials model of deep-learning density functional theory Hamiltonian

Realizing large materials models has emerged as a critical endeavor for materials research in the new era of artificial intelligence,but how to achieve this fantastic and challenging objective remains elusive.Here,we propose a feasible pathway to address this paramount pursuit by developing universal materials models of deep-learning density functional theory Hamiltonian(Deep H),enabling computational modeling of the complicated structure-property relationship of materials in general.By constructing a large materials database and substantially improving the Deep H method,we obtain a universal materials model of Deep H capable of handling diverse elemental compositions and material structures,achieving remarkable accuracy in predicting material properties.We further showcase a promising application of fine-tuning universal materials models for enhancing specific materials models.This work not only demonstrates the concept of Deep H's universal materials model but also lays the groundwork for developing large materials models,opening up significant opportunities for advancing artificial intelligencedriven materials discovery.