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

Molecular hydrogen in the N-doped LuH3 system as a possible path to superconductivity

The discovery of ambient superconductivity would mark an epochal breakthrough long-awaited for over a century, potentially ushering in unprecedented scientific and technological advancements. The recent findings on high-temperature superconducting phases in various hydrides under high pressure have ignited optimism, suggesting that the realization of near-ambient superconductivity might be on the horizon. However, the preparation of hydride samples tends to promote the emergence of various metastable phases, marked by a low level of experimental reproducibility. Identifying these phases through theoretical and computational methods entails formidable challenges, often resulting in controversial outcomes. In this paper, we consider N-doped LuH3 as a prototypical complex hydride: By means of machine-learning-accelerated force-field molecular dynamics, we have identified the formation of H2 molecules stabilized at ambient pressure by nitrogen impurities. Importantly, we demonstrate that this molecular phase plays a pivotal role in the emergence of a dynamically stable, low-temperature, experimental-ambient-pressure superconductivity. The potential to stabilize hydrogen in molecular form through chemical doping opens up a novel avenue for investigating disordered phases in hydrides and their transport properties under near-ambient conditions.

AtomGPT: Atomistic Generative Pretrained Transformer for Forward and Inverse Materials Design

Large language models (LLMs) such as generative pretrained transformers (GPTs) have shown potential for various commercial applications, but their applicability for materials design remains underexplored. In this Letter, AtomGPT is introduced as a model specifically developed for materials design based on transformer architectures, demonstrating capabilities for both atomistic property prediction and structure generation. This study shows that a combination of chemical and structural text descriptions can efficiently predict material properties with accuracy comparable to graph neural network models, including formation energies, electronic bandgaps from two different methods, and superconducting transition temperatures. Furthermore, AtomGPT can generate atomic structures for tasks such as designing new superconductors, with the predictions validated through density functional theory calculations. This work paves the way for leveraging LLMs in forward and inverse materials design, offering an efficient approach to the discovery and optimization of materials.

Low Temperature Synthesis of 2D p-Type α-In2Te3 with Fast and Broadband Photodetection

2DA 2 III B 3 VI ${\mathrm{A}}_2^{{\mathrm{III}}}{\mathrm{B}}_3^{{\mathrm{VI}}}$ compounds (A = Al, Ga, In, and B = S, Se, and Te) with intrinsic structural defects offer significant opportunities for high-performance and functional devices. However, obtaining 2D atomic-thin nanoplates with non-layered structure on SiO2/Si substrate at low temperatures is rare, which hinders the study of their properties and applications at atomic-thin thickness limits. In this study, the synthesis of ultrathin, non-layered α-In2Te3 nanoplates is demonstrated using a BiOCl-assisted chemical vapor deposition method at a temperature below 350 °C on SiO2/Si substrate. Comprehensive characterization results confirm the high-quality single crystal is the low-temperature cubic phase α-In2Te3 , possessing a noncentrosymmetric defected ZnS structure with good second harmonic generation. Moreover, α-In2Te3 is revealed to be a p-type semiconductor with a direct and narrow bandgap value of 0.76 eV. The field effect transistor exhibits a high mobility of 18 cm2 V-1 s-1, and the photodetector demonstrates stable photoswitching behavior within a broadband photoresponse from 405 to 1064 nm, with a satisfactory response time of τrise = 1 ms. Notably, the α-In2Te3 nanoplates exhibit good stability against ambient environments. Together, these findings establish α-In2Te3 nanoplates as promising candidates for next-generation high-performance photonics and electronics.

Strong electron-phonon coupling and multigap superconductivity in 2H/1T Janus MoSLi monolayer

Two-dimensional (2D) Janus transition metal dichalcogenides MXY manifest novel physical properties owing to the breaking of out-of-plane mirror symmetry. Recently, the 2H phase of MoSH has been demonstrated to possess intrinsic superconductivity, whereas the 1T phase exhibits a charge density waves state. In this paper, we have systematically studied the stability and electron-phonon interaction characteristics of MoSLi. Our results have shown that both the 2H and 1T phases of MoSLi are stable, as indicated by the phonon spectrum and the ab initio molecular dynamics. However, the 1T phase exhibits an electron-phonon coupling constant that is twice as large as that of the 2H phase. In contrast to MoSH, the 1T phase of MoSLi exhibits intrinsic superconductivity. By employing the ab initio anisotropic Migdal-Eliashberg formalism, we have revealed the two-gap superconducting nature of 1T-MoSLi, with a transition temperature (Tc) of 14.8 K. The detailed analysis indicates that the superconductivity in 1T-MoSLi primarily originates from the interplay between the vibration of the phonon modes in the low-frequency region and the dz2 orbital. These findings provide a fresh perspective on superconductivity within Janus structures.

Prediction of superconductivity in a series of tetragonal transition metal dichalcogenides

Transition metal dichalcogenides (TMDCs) represent a well-known material family with diverse structural phases and rich electronic properties; they are thus an ideal platform for studying the emergence and exotic phenomenon of superconductivity (SC). Herein, we propose the existence of tetragonal TMDCs with a distorted Lieb (dLieb) lattice structure and the stabilized transition metal disulfides (MS2), including dLieb-ZrS2, dLieb-NbS2, dLieb-MnS2, dLieb-FeS2, dLieb-ReS2, and dLieb-OsS2. Except for semiconducting dLieb-ZrS2 and magnetic dLieb-MnS2, the rest of metallic dLieb-MS2 was found to exhibit intrinsic SC with the transition temperature (TC) ranging from ∼5.4 to ∼13.0 K. The TC of dLieb-ReS2 and dLieb-OsS2 exceeded 10 K and was higher than that of the intrinsic SC in the known metallic TMDCs, which is attributed to the significant phonon-softening enhanced electron-phonon coupling strength. Different from the Ising spin-orbit coupling (SOC) effect in existing non-centrosymmetric TMDCs, the non-magnetic dLieb-MS2 monolayers exhibit the Dresselhaus SOC effect, which is featured by in-plane spin orientations and will give rise to the topological SC under proper conditions. In addition to enriching the structural phases of TMDCs, our work predicts a series of SC candidates with high intrinsic TC and topological non-triviality used for fault-tolerant quantum computation.

Strain-induced ferroelectric polarization reversal without undergoing geometric inversion in blue SiSe monolayer

Ferroelectricity in two-dimensional (2D) systems generally arises from phonons and has been widely investigated. On the contrary, electronic ferroelectricity in 2D systems has been rarely studied. Using first-principles calculations, the ferroelectric behavior of the buckled blue SiSe monolayer under strain are explored. It is found that the direction of the out-of-plane ferroelectric polarization can be reversed by applying an in-plane strain. And such polarization switching is realized without undergoing geometric inversion. Besides, the strain-triggered polarization reversal emerges in both biaxial and uniaxial strain cases, indicating it is an intrinsic feature of such a system. Further analysis shows that the polarization switching is the result of the reversal of the magnitudes of the positive and negative charge center vectors. And the variation of buckling is found to play an important role, which results in the switch. Moreover, a non-monotonic variation of band gap with strain is revealed. Our findings throws light on the investigation of novel electronic ferroelectric systems.

Sampling the Materials Space for Conventional Superconducting Compounds

A large scale study of conventional superconducting materials using a machine-learning accelerated high-throughput workflow is performed, starting by creating a comprehensive dataset of around 7000 electron-phonon calculations performed with reasonable convergence parameters. This dataset is then used to train a robust machine learning model capable of predicting the electron-phonon and superconducting properties based on structural, compositional, and electronic ground-state properties. Using this machine, the transition temperatures (Tc ) of approximately 200 000 metallic compounds are evaluated, all of which are on the convex hull of thermodynamic stability (or close to it) to maximize the probability of synthesizability. Compounds predicted to have Tc values exceeding 5 K are further validated using density-functional perturbation theory. As a result, 541 compounds with Tc values surpassing 10 K, encompassing a variety of crystal structures and chemical compositions, are identified. This work is complemented with a detailed examination of several interesting materials, including nitrides, hydrides, and intermetallic compounds. Particularly noteworthy is LiMoN2 , which is predicted to be superconducting in the stoichiometric trigonal phase, with a Tc exceeding 38 K. LiMoN2 has previously been synthesized in this phase, further heightening its potential for practical applications.

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.

High-Throughput Computational Design of 2D Ternary Chalcogenides for Sustainable Energy

Two-dimensional materials are considered to be promising for next-generation electronic and energy devices. However, the limited availability of 2D materials hinders their applications. Herein, we employed high-throughput computation to discover new 2D materials by cleaving the bulk and to investigate their electronic, thermoelectric, and optoelectronic properties. Using our database containing 810 structures of chalcogenides ABX3 (A or B = Al, Ga, In, Si, Ge, Sn, P, As, Sb, and Bi; X = S, Se, and Te), we identified 204 new 2D compounds promising for experimental preparation according to the exfoliation energy. Notably, 96 of them are more easily exfoliated than graphene, 52 compounds show higher Seebeck coefficients than Bi2Te3 at 300 K, and 20 compounds have power factors beyond 2 × 10-3 Wm-1 K-2 at 900 K. Also, 6 new compounds exhibit high theoretical photovoltaic efficiency exceeding 30%. Our findings expand the 2D materials family and provide new 2D compounds for sustainable thermoelectric and optoelectronic energy applications.

Competing length scales and 2D versus 3D dimensionality in relatively thick superconducting NbN films

Magneto-transport characteristics of 2D and 3D superconducting layers, in particular, temperature and angular dependences of the upper critical field Hc2, are usually considered to be fundamentally different. In the work, using non-local resistance measurements at temperatures near the normal-to-superconducting transition, we probed an effective dimensionality of nm-thick NbN films. It was found that in relatively thick NbN layers, the thicknesses of which varied from 50 to 100 nm, the temperature effect on Hc2 certainly pointed to the three-dimensionality of the samples, while the angular dependence of Hc2 revealed behavior typical for 2D samples. The seeming contradiction is explained by an intriguing interplay of three length scales in the dimensionally confined superconducting films: the thickness, the Ginzburg-Landau coherence length, and the magnetic-field penetration depth. Our results provide new insights into the physics of superconducting films with an extremely large ratio of the London penetration depth to the Ginzburg-Landau coherence length exhibiting simultaneously 3D isotropic superconducting properties and the 2D transport regime.

ColabFit exchange: Open-access datasets for data-driven interatomic potentials

Data-driven interatomic potentials (IPs) trained on large collections of first principles calculations are rapidly becoming essential tools in the fields of computational materials science and chemistry for performing atomic-scale simulations. Despite this, apart from a few notable exceptions, there is a distinct lack of well-organized, public datasets in common formats available for use with IP development. This deficiency precludes the research community from implementing widespread benchmarking, which is essential for gaining insight into model performance and transferability, and also limits the development of more general, or even universal, IPs. To address this issue, we introduce the ColabFit Exchange, the first database providing open access to a large collection of systematically organized datasets from multiple domains that is especially designed for IP development. The ColabFit Exchange is publicly available at https://colabfit.org, providing a web-based interface for exploring, downloading, and contributing datasets. Composed of data collected from the literature or provided by community researchers, the ColabFit Exchange currently (September 2023) consists of 139 datasets spanning nearly 70 000 unique chemistries, and is intended to continuously grow. In addition to outlining the software framework used for constructing and accessing the ColabFit Exchange, we also provide analyses of the data, quantifying the diversity of the database and proposing metrics for assessing the relative diversity of multiple datasets. Finally, we demonstrate an end-to-end IP development pipeline, utilizing datasets from the ColabFit Exchange, fitting tools from the KLIFF software package, and validation tests provided by the OpenKIM framework.

TribChem: A Software for the First-Principles, High-Throughput Study of Solid Interfaces and Their Tribological Properties

High-throughput first-principles calculations, based on solving the quantum mechanical many-body problem for hundreds of materials in parallel, have been successfully applied to advance many materials-based technologies, from batteries to hydrogen storage. However, this approach has not yet been adopted to systematically study solid-solid interfaces and their tribological properties. To this aim, we developed TribChem, an advanced software program based on the FireWorks platform, which is here presented and released. TribChem is constructed in a modular way, allowing for the separate calculation of bulk, surface, and interface properties. At present, the calculated interfacial properties include adhesion, shear strength, and charge redistribution. Further properties can be easily added due to the general structure of the main workflow. TribChem contains a high-level interface class to store/retrieve results from its own database and connect to public databases.

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

Finding new superconductors with a high critical temperature (T_c) 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.

High-Throughput First-Principles Prediction of Interfacial Adhesion Energies in Metal-on-Metal Contacts

Adhesion energy, a measure of the strength by which two surfaces bind together, ultimately dictates the mechanical behavior and failure of interfaces. As natural and artificial solid interfaces are ubiquitous, adhesion energy represents a key quantity in a variety of fields ranging from geology to nanotechnology. Because of intrinsic difficulties in the simulation of systems where two different lattices are matched, and despite their importance, no systematic, accurate first-principles determination of heterostructure adhesion energy is available. We have developed robust, automatic high-throughput workflow able to fill this gap by systematically searching for the optimal interface geometry and accurately determining adhesion energies. We apply it here for the first time to perform the screening of around a hundred metallic heterostructures relevant for technological applications. This allows us to populate a database of accurate values, which can be used as input parameters for macroscopic models. Moreover, it allows us to benchmark commonly used, empirical relations that link adhesion energies to the surface energies of its constituent and to improve their predictivity employing only quantities that are easily measurable or computable.