High-Throughput DFT-Based Discovery of Next Generation Two-Dimensional (2D) Superconductors.

High-throughput density functional theory (DFT) calculations allow for a systematic search for conventional superconductors. With the recent interest in two-dimensional (2D) superconductors, we used a high-throughput workflow to screen over 1000 2D materials in the JARVIS-DFT database and performed electron-phonon coupling calculations, using the McMillan-Allen-Dynes formula to calculate the superconducting transition temperature (Tc) for 165 of them. Of these 165 materials, we identify 34 dynamically stable structures with transition temperatures above 5 K, including materials such as W2N3, NbO2, ZrBrO, TiClO, NaSn2S4, Mg2B4C2, and the previously unreported Mg2B4N2 (Tc = 21.8 K). Finally, we performed experiments to determine the Tc of selected layered superconductors (2H-NbSe2, 2H-NbS2, ZrSiS, FeSe) and discuss the measured results within the context of our DFT results. We aim that the outcome of this workflow can guide future computational and experimental studies of new and emerging 2D superconductors by providing a roadmap of high-throughput DFT data.

A pathway toward high-throughput quantum Monte Carlo simulations for alloys: A case study of two-dimensional (2D) GaSxSe1-x.

The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering of the different structures experimentally. In order to combat this, first-principles methods have been coupled with statistical methods such as the cluster expansion formalism in order to construct the energy hull diagram, which helps to determine if an alloyed structure can exist in nature. Traditionally, density functional theory (DFT) has been used in such workflows. In this paper, we propose to use chemically accurate many-body variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC) methods to construct the energy hull diagram of an alloy system due to the fact that such methods have a weaker dependence on the starting wavefunction and density functional, scale similarly to DFT with the number of electrons, and have had demonstrated success for a variety of materials. To carry out these simulations in a high-throughput manner, we propose a method called Jastrow sharing, which involves recycling the optimized Jastrow parameters between alloys with different stoichiometries. We show that this eliminates the need for extra VMC Jastrow optimization calculations and results in significant computational cost savings (on average 1/4 savings of total computational time). Since it is a novel post-transition metal chalcogenide alloy series that has been synthesized in its few-layer form, we used monolayer GaSxSe1-x as a case study for our workflow. By extensively testing our Jastrow sharing procedure for monolayer GaSxSe1-x and quantifying the cost savings, we demonstrate how a pathway toward chemically accurate high-throughput simulations of alloys can be achieved using many-body VMC and DMC methods.

Electronic properties of bare and functionalized two-dimensional (2D) tellurene structures.

Recently, 2D tellurene (Te) structures have been experimentally synthesized. These structures possess high carrier mobility and stability which make them ideal candidates for applications in electronics, optoelectronics and energy devices. We performed density functional theory (DFT) and molecular dynamics (MD) simulations to investigate the stability and electronic structure of 2D α- and ß-Te sheets, and hydrogen, oxygen, and fluorine functionalized counterparts, including spin-orbit coupling effects. Our calculations show that bare α and ß-Te sheets are stable with band gaps of 0.44 eV and 1.02 eV respectively. When functionalized, α and ß monolayers exhibit metallic properties, except for hydrogenated ß-Te, which exhibits semiconducting properties with a band gap of 1.37 eV. We see that H, O and F destabilize the structure of α-Te. We also find that F and H cause ß-Te layers to separate into functionalized atomic chains and O causes ß-Te to transform into a Te3O2-like structure. We also studied single atom and molecule binding on the Te surface, the effects of adatom coverage, and the effects of functionalized Te on a GaSe substrate. Our results indicate that tellurene monolayers and functionalized counterparts are not only suitable for future optoelectronic devices, but can be used as metallic contacts in nanoscale junctions.

A first-principles Quantum Monte Carlo study of two-dimensional (2D) GaSe.

Two-dimensional (2D) post-transition metal chalcogenides (PTMCs) have attracted attention due to their suitable bandgaps and lower exciton binding energies, making them more appropriate for electronic, optical, and water-splitting devices than graphene and monolayer transition metal dichalcogenides. Of the predicted 2D PTMCs, GaSe has been reliably synthesized and experimentally characterized. Despite this fact, quantities such as lattice parameters and band character vary significantly depending on which density functional theory (DFT) functional is used. Although many-body perturbation theory (GW approximation) has been used to correct the electronic structure and obtain the excited state properties of 2D GaSe, and solving the Bethe-Salpeter equation (BSE) has been used to find the optical gap, we find that the results depend strongly on the starting wavefunction. In an attempt to correct these discrepancies, we employed the many-body Diffusion Monte Carlo (DMC) method to calculate the ground and excited state properties of GaSe because DMC has a weaker dependence on the trial wavefunction. We benchmark these results with available experimental data, DFT [local-density approximation, Perdew-Burke-Ernzerhof (PBE), strongly constrained and appropriately normed (SCAN) meta-GGA, and hybrid (HSE06) functionals] and GW-BSE (using PBE and SCAN wavefunctions) results. Our findings confirm that monolayer GaSe is an indirect gap semiconductor (Γ-M) with a quasiparticle electronic gap in close agreement with experiment and low exciton binding energy. We also benchmark the optimal lattice parameter, cohesive energy, and ground state charge density with DMC and various DFT methods. We aim to present a terminal theoretical benchmark for pristine monolayer GaSe, which will aid in the further study of 2D PTMCs using DMC methods.

Data-driven Design of High Pressure Hydride Superconductors using DFT and Deep Learning.

The observation of superconductivity in hydride-based materials under ultrahigh pressures (for example, H3S and LaH10) has fueled the interest in a more data-driven approach to discovering new high-pressure hydride superconductors. In this work, we performed density functional theory (DFT) calculations to predict the critical temperature (Tc) of over 900 hydride materials under a pressure range of (0 to 500) GPa, where we found 122 dynamically stable structures with a Tc above MgB2 (39 K). To accelerate screening, we trained a graph neural network (GNN) model to predict Tc and demonstrated that a universal machine learned force-field can be used to relax hydride structures under arbitrary pressures, with significantly reduced cost. By combining DFT and GNNs, we can establish a more complete map of hydrides under pressure.

Systematic DFT+U and Quantum Monte Carlo Benchmark of Magnetic Two-Dimensional (2D) CrX3 (X = I, Br, Cl, F).

The search for two-dimensional (2D) magnetic materials has attracted a great deal of attention because of the experimental synthesis of 2D CrI3, which has a measured Curie temperature of 45 K. Often times, these monolayers have a higher degree of electron correlation and require more sophisticated methods beyond density functional theory (DFT). Diffusion Monte Carlo (DMC) is a correlated electronic structure method that has been demonstrated to be successful for calculating the electronic and magnetic properties of a wide variety of 2D and bulk systems, since it has a weaker dependence on the Hubbard parameter (U) and density functional. In this study, we designed a workflow that combines DFT +U and DMC in order to treat 2D correlated magnetic systems. We chose monolayer CrX3 (X = I, Br, Cl, F), with a stronger focus on CrI3 and CrBr3, as a case study due to the fact that they have been experimentally realized and have a finite critical temperature. With this DFT+U and DMC workflow and the analytical method of Torelli and Olsen, we estimated a maximum value of 43.56 K for the Tc of CrI3 and 20.78 K for the Tc of CrBr3, in addition to analyzing the spin densities and magnetic properties with DMC and DFT+U. We expect that running this workflow for a well-known material class will aid in the future discovery and characterization of lesser known and more complex correlated 2D magnetic materials.

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models.

Finding new superconductors with a high critical temperature (Tc) has been a challenging task due to computational and experimental costs. We present a diffusion model inspired by the computer vision community to generate new superconductors with unique structures and chemical compositions. Specifically, we used a crystal diffusion variational autoencoder (CDVAE) along with atomistic line graph neural network (ALIGNN) pretrained models and the Joint Automated Repository for Various Integrated Simulations (JARVIS) superconducting database of density functional theory (DFT) calculations to generate new superconductors with a high success rate. We started with a DFT data set of ∼1000 superconducting materials to train the diffusion model. We used the model to generate 3000 new structures, which along with pretrained ALIGNN screening results in 61 candidates. For the top candidates, we performed DFT calculations for validation. Such approaches go beyond funnel-like materials screening approaches and allow for the inverse design of next-generation materials.

A Quantum Monte Carlo Study of the Structural, Energetic, and Magnetic Properties of Two-Dimensional H and T Phase VSe2.

Previous works have controversially claimed near-room-temperature ferromagnetism in two-dimensional (2D) VSe2, with conflicting results throughout the literature. These discrepancies in magnetic properties between both phases (T and H) of 2D VSe2 are most likely due to the structural parameters being coupled to the magnetic properties. Specifically, both phases have a close lattice match and similar total energies, which makes it difficult to determine which phase is being observed experimentally. In this study, we used a combination of density functional theory, highly accurate diffusion Monte Carlo (DMC), and a surrogate Hessian line-search optimization technique to resolve the previously reported discrepancy in structural parameters and relative phase stability. With DMC accuracy, we determined the free-standing geometry of both phases and constructed a phase diagram. Our findings demonstrate the successes of the DMC method coupled with the surrogate Hessian structural optimization technique when applied to a 2D magnetic system.

Abnormal Phase Transition and Band Renormalization of Guanidinium-Based Organic-Inorganic Hybrid Perovskite.

Low-dimensional organic-inorganic hybrid perovskites have attracted much interest owing to their superior solar conversion performance, environmental stability, and excitonic properties compared to their three-dimensional (3D) counterparts. Among reduced-dimensional perovskites, guanidinium-based perovskites crystallize in layered one-dimensional (1D) and two-dimensional (2D). Here, our studies demonstrate how the dimensionality of the hybrid perovskite influences the chemical and physical properties under different pressures (i.e., bond distance, angle, vdW distance). Comprehensive studies show that 1D GuaPbI3 does not undergo a phase transition even up to high pressures (∼13 GPa) and its band gap monotonically reduces with pressure. In contrast, 2D Gua2PbI4 exhibits an early phase transition at 5.5 GPa and its band gap follow nonmonotonic pressure response associated with phase transition as well as other bond angle changes. Computational simulations reveal that the phase transition is related to the structural deformation and rotation of PbI6 octahedra in 2D Gua2PbI4 owing to a larger degree of freedom of deformation. The soft lattice allows them to uptake large pressures, which renders structural phase transitions possible. Overall the results offer the first insights into how layered perovskites with different dimensionality respond to structural changes driven by pressure.

Engineering the Electronic, Thermoelectric, and Excitonic Properties of Two-Dimensional Group-III Nitrides through Alloying for Optoelectronic Devices (B1-xAlxN, Al1-xGaxN, and Ga1-xInxN).

Recently, two-dimensional (2D) group-III nitride semiconductors such as h-BN, h-AlN, h-GaN, and h-InN have attracted attention because of their exceptional electronic, optical, and thermoelectric properties. It has also been demonstrated, theoretically and experimentally, that properties of 2D materials can be controlled by alloying. In this study, we performed density functional theory (DFT) calculations to investigate 2D B1-xAlxN, Al1-xGaxN, and Ga1-xInxN alloyed structures. We also calculated the thermoelectric properties of these structures using Boltzmann transport theory based on DFT and the optical properties using the GW method and the Bethe-Salpeter equation. We find that by changing the alloying concentration, the band gap and exciton binding energies of each structure can be tuned accordingly, and for certain concentrations, a high thermoelectric performance is reported with strong dependence on the effective mass of the given alloyed monolayer. In addition, the contribution of each e-h pair is explained by investigating the e-h coupling strength projected on the electronic band structure, and we find that the exciton binding energy decreases with increase in sequential alloying concentration. With the ability to control such properties by alloying 2D group-III nitrides, we believe that this work will play a crucial role for experimentalists and manufacturers focusing on next-generation electronic, optoelectronic, and thermoelectric devices.