High-throughput discovery of high Curie point two-dimensional ferromagnetic materials

Databases for two-dimensional materials host numerous ferromagnetic materials without the vital information of Curie temperature since its calculation involves a manually intensive complex process.In this work,we develop a fully automated,hardwareaccelerated,dynamic-translation based computer code,which performs first principles-based computations followed by Heisenberg model-based Monte Carlo simulations to estimate the Curie temperature from the crystal structure.We employ this code to conduct a high-throughput scan of 786 materials from a database to discover 26 materials with a Curie point beyond 400 K.For rapid data mining,we further use these results to develop an end-to-end machine learning model with generalized chemical features through an exhaustive search of the model space as well as the hyperparameters.We discover a few more high Curie point materials from different sources using this data-driven model.Such material informatics,which agrees well with recent experiments,is expected to foster practical applications of two-dimensional magnetism.

High-throughput design of functional-engineered MXene transistors with low-resistive contacts

Two-dimensional material-based transistors are being extensively investigated for CMOS(complementary metal oxide semiconductor)technology extension;nevertheless,downscaling appears to be challenging owing to high metal-semiconductor contact resistance.Here,we propose a functional group-engineered monolayer transistor architecture that takes advantage of MXenes’natural material chemistry to offer low-resistive contacts.We design an automated,high-throughput computational pipeline that first performs hybrid density functional theory-based calculations to find 16 sets of complementary transistor configurations by screening more than 23,000 materials from an MXene database and then conducts self-consistent quantum transport calculations to simulate their current-voltage characteristics for channel lengths ranging from 10 nm to 3 nm.Performance of these devices has been found to meet the requirements of the international roadmap for devices and systems(IRDS)for several benchmark metrics(on current,power dissipation,delay,and subthreshold swing).The proposed balanced-mode,functional-engineered MXene transistors may lead to a realistic solution for the sub-decananometer technology scaling by enabling doping-free intrinsically low contact resistance.

Realizing unipolar and bipolar intrinsic skyrmions in MXenes from high-fidelity first-principles calculations

Magnetic skyrmions,which are topologically protected tiny spin textures,have emerged as information carriers in energy-efficient logic and memory devices.Skyrmions are commonly realized by inducing large Dzyaloshinskii–Moriya interaction(DMI)in the interface of heavy metal heterolayers.With the advent of two-dimensional magnetism,it is being envisioned to host intrinsic skyrmions in a monolayer,which will be free from any interfacial defect and stacking order.Here using high-fidelity exchange-correlation functional-based first-principles calculations,we investigate such a possibility in methodically designed non-centrosymmetric MXene structures.From a search space of about 3000 materials,our customized high-throughput computational pipeline systematically harnesses out-of-the-plane and in-plane magnetism along with strong DMI to realize typical‘unipolar’skyrmions in 78 materials and exotic‘bipolar’skyrmions in 13 materials.Micromagnetic and atomistic Monte Carlo simulations further reveal that skyrmions in some of these materials may be stable at room temperature without any external magnetic field.Our study may pave the way for the practical realization of skyrmions-based information technology.