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.

Numerical quality control for DFT-based materials databases

Electronic-structure theory is a strong pillar of materials science.Many different computer codes that employ different approaches are used by the community to solve various scientific problems.Still,the precision of different packages has only been scrutinized thoroughly not long ago,focusing on a specific task,namely selecting a popular density functional,and using unusually high,extremely precise numerical settings for investigating 71 monoatomic crystals^(1).Little is known,however,about method- and code-specific uncertainties that arise under numerical settings that are commonly used in practice.We shed light on this issue by investigating the deviations in total and relative energies as a function of computational parameters.Using typical settings for basis sets and k-grids,we compare results for 71 elemental^(1) and 63 binary solids obtained by three different electronic-structure codes that employ fundamentally different strategies.On the basis of the observed trends,we propose a simple,analytical model for the estimation of the errors associated with the basis-set incompleteness.We cross-validate this model using ternary systems obtained from the Novel Materials Discovery (NOMAD) Repository and discuss how our approach enables the comparison of the heterogeneous data present in computational materials databases.