High-throughput computational screening for twodimensional magnetic materials based on experimental databases of three-dimensional compounds

We perform a computational screening for two-dimensional(2D)magnetic materials based on experimental bulk compounds present in the Inorganic Crystal Structure Database and Crystallography Open Database.A recently proposed geometric descriptor is used to extract materials that are exfoliable into 2D derivatives and we find 85 ferromagnetic and 61 antiferromagnetic materials for which we obtain magnetic exchange and anisotropy parameters using density functional theory.For the easy-axis ferromagnetic insulators we calculate the Curie temperature based on a fit to classical Monte Carlo simulations of anisotropic Heisenberg models.We find good agreement with the experimentally reported Curie temperatures of known 2D ferromagnets and identify 10 potentially exfoliable 2D ferromagnets that have not been reported previously.In addition,we find 18 easy-axis antiferromagnetic insulators with several compounds exhibiting very strong exchange coupling and magnetic anisotropy.

Shift current photovoltaic efficiency of 2D materials

Shift current photovoltaic devices are potential candidates for future cheap,sustainable,and efficient electricity generation.In the present work,we calculate the solar-generated shift current and efficiencies in 326 different 2D materials obtained from the computational database C2DB.We apply,as metrics,the efficiencies of monolayer and multilayer samples.The monolayer efficiencies are generally found to be low,while the multilayer efficiencies of infinite stacks show great promise.Furthermore,the out-of-plane shift current response is considered,and material candidates for efficient out-of-plane shift current devices are identified.Among the screened materials,MXY Janus and MX_(2) transition metal dichalchogenides(TMDs)constitute a prominent subset,with chromium based MXY Janus TMDs holding particular promise.Finally,in order to explain the band gap dependence of the PV efficiency,a simple gapped graphene model with a variable band gap is established and related to the calculated efficiencies.

Data-driven discovery of 2D materials by deep generative models

Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery.Here,we show that a crystal diffusion variational autoencoder(CDVAE)is capable of generating two-dimensional(2D)materials of high chemical and structural diversity and formation energies mirroring the training structures.Specifically,we train the CDVAE on 26152D materials with energy above the convex hullΔH_(hull)<0.3 eV/atom,and generate 5003 materials that we relax using density functional theory(DFT).We also generate 14192 new crystals by systematic element substitution of the training structures.We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions.In total we find 11630 predicted new 2D materials,where 8599 of these haveΔH_(hull)<0.3 eV/atom as the seed structures,while 2004 are within 50 meV of the convex hull and could potentially be synthesised.The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database(C2DB).Our work establishes the CDVAE as an efficient and reliable crystal generation machine,and significantly expands the space of 2D materials.

Beyond the RPA and GW methods with adiabatic xc-kernels for accurate ground state and quasiparticle energies

We review the theory and application of adiabatic exchange–correlation(xc)-kernels for ab initio calculations of ground state energies and quasiparticle excitations within the frameworks of the adiabatic connection fluctuation dissipation theorem and Hedin’s equations,respectively.Various different xc-kernels,which are all rooted in the homogeneous electron gas,are introduced but hereafter we focus on the specific class of renormalized adiabatic kernels,in particular the rALDA and rAPBE.The kernels drastically improve the description of short-range correlations as compared to the random phase approximation(RPA),resulting in significantly better correlation energies.This effect greatly reduces the reliance on error cancellations,which is essential in RPA,and systematically improves covalent bond energies while preserving the good performance of the RPA for dispersive interactions.For quasiparticle energies,the xc-kernels account for vertex corrections that are missing in the GW self-energy.In this context,we show that the short-range correlations mainly correct the absolute band positions while the band gap is less affected in agreement with the known good performance of GW for the latter.The renormalized xc-kernels offer a rigorous extension of the RPA and GW methods with clear improvements in terms of accuracy at little extra computational cost.