Ferroelectric and Room-Temperature Ferromagnetic Semiconductors in the 2D MIMIIGe2X6 Family: First-Principles and Machine Learning Investigations

Machine Learning-Assisted Exploration of Intrinsically Spin-Ordered Two-Dimensional (2D) Nanomagnets

The existence of spontaneous spin-ordering in two-dimensional (2D) nanomagnets holds significant importance due to their several unique and promising properties that distinguish them from conventional 2D materials. In recent times, machine learning (ML) has emerged as a powerful tool for effectively exploring and identifying the optimal 2D materials for specific applications or properties within a limited span of time. Here, we have introduced ML-accelerated approaches to specifically estimate the properties, such as the HSE bandgap and magnetoanisotropic energy (MAE) of 2D magnetic materials. Supervised ML algorithms were employed to derive the descriptors that are capable of predicting the properties of intrinsic 2D magnetic materials. Furthermore, the feature selection score is also calculated to reduce the feature space complexity and improve the model accuracy. The input features were obtained from the C2DB database, and models were constructed using linear regression, Lasso, decision tree, random forest, XG Boost, and support vector machine algorithms. The random forest model predicted the HSE band gaps with an unprecedented low root-mean-square error (RMSE) of 0.22 eV, while the linear regression gives the best fit with RMSEs of 0.25 and 0.22 meV for the MAE(x) and MAE(y), respectively. Therefore, the integration of interpretable analytical models with density functional theory offers a swift and reliable approach for uncovering the properties of intrinsic 2D magnetic materials. This collaborative methodology not only ensures speed in analysis but also enriches the material space.

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Engineering Layertronics in Two-Dimensional Ferromagnetic Multiferroic Lattice

Layertronics, rooted in the layer Hall effect (LHE), is an emerging fundamental phenomenon in condensed matter physics and spintronics. So far, several theoretical and experimental proposals have been made to realize LHE, but all are based on antiferromagnetic systems. Here, using symmetry and tight-binding model analysis, we propose a general mechanism for engineering layertronics in a two-dimensional ferromagnetic multiferroic lattice. The physics is related to the band geometric properties and multiferroicity, which results in the coupling between Berry curvature and layer degree of freedom, thereby generating the LHE. Using first-principles calculations, we further demonstrate this mechanism in bilayer (BL) TcIrGe2S6. Due to the intrinsic inversion and time-reversal symmetry breakings, BL TcIrGe2S6 exhibits multiferroicity with large Berry curvatures at both the center and corners of the Brillouin zone. These Berry curvatures couple with the layer physics, forming the LHE in BL TcIrGe2S6. Our work opens a new direction for research on layertronics.

Ferroic Phases in Two-Dimensional Materials

Two-dimensional (2D) ferroics, namely ferroelectric, ferromagnetic, and ferroelastic materials, are attracting rising interest due to their fascinating physical properties and promising functional applications. A variety of 2D ferroic phases, as well as 2D multiferroics and the novel 2D ferrovalleytronics/ferrotoroidics, have been recently predicted by theory, even down to the single atomic layers. Meanwhile, some of them have already been experimentally verified. In addition to the intrinsic 2D ferroics, appropriate stacking, doping, and defects can also artificially regulate the ferroic phases of 2D materials. Correspondingly, ferroic ordering in 2D materials exhibits enormous potential for future high density memory devices, energy conversion devices, and sensing devices, among other applications. In this paper, the recent research progresses on 2D ferroic phases are comprehensively reviewed, with emphasis on chemistry and structural origin of the ferroic properties. In addition, the promising applications of the 2D ferroics for information storage, optoelectronics, and sensing are also briefly discussed. Finally, we envisioned a few possible pathways for the future 2D ferroics research and development. This comprehensive overview on the 2D ferroic phases can provide an atlas for this field and facilitate further exploration of the intriguing new materials and physical phenomena, which will generate tremendous impact on future functional materials and devices.

Recent progress in the theoretical design of two-dimensional ferroelectric materials

Two-dimensional (2D) ferroelectrics (FEs), which maintain stable electric polarization in ultrathin films, are a promising class of materials for the development of various miniature functional devices. In recent years, several 2D FEs with unique properties have been successfully fabricated through experiments. They have been found to exhibit some unique properties either by themselves or when they are coupled with other functional materials (e.g., ferromagnetic materials, materials with 5d electrons, etc.). As a result, several new types of 2D FE functional devices have been developed, exhibiting excellent performance. As a type of newly discovered 2D functional material, the number of 2D FEs and the exploration of their properties are still limited and this calls for further theoretical predictions. This review summarizes recent progress in the theoretical predictions of 2D FE materials and provides strategies for the rational design of 2D FE materials. The aim of this review is to provide guidelines for the design of 2D FE materials and related functional devices.

Ferroelectricity and High Curie Temperature in a 2D Janus Magnet

The breaking of the out-of-plane symmetry makes a two-dimensional (2D) Janus monolayer a new platform to explore the coupling between ferroelectricity and ferromagnetism. Using density functional theory in combination with Monte Carlo simulations, we report a novel phase-switchable 2D multiferroic material VInSe3 with large intrinsic out-of-plane spontaneous electric polarization and a high Curie temperature (Tc). The structural transition energy barrier between the two phases is determined to be 0.4 eV, indicating the switchability of the electric polarizations and the potential ferroelectricity. Carrier doping can boost the Curie temperature above room temperature, attributing to the enhanced magnetic exchange interaction. A transition from the ferromagnetic (FM) state to the antiferromagnetic (AFM) state can be induced by carrier doping in octahedra-VInSe3, while FM coupling is well-preserved in tetrahedron-VInSe3, which can be regulated to be either an XY or Ising magnet at an appropriate carrier concentration. These findings not only enrich the family of high-Tc low-dimensional monolayers but also offer a new direction for the design and multifunctional application of multiferroic materials.

Two-Dimensional Intercalating Multiferroics with Strong Magnetoelectric Coupling

Intrinsic two-dimensional (2D) multiferroics that couple ferromagnetism and ferroelectricity are rare. Here, we present an approach to achieve 2D multiferroics using powerful intercalation technology. In this approach, metal atoms such as Cu or Ag atoms are intercalated in bilayer CrI₃ to form Cu(CrI₃)₄ or Ag(CrI₃)₄. The intercalant leads to the inversion symmetry breaking and produces a large out-of-plane electric polarization with a low transition barrier and a small reversal electric field, exhibiting excellent 2D ferroelectric properties. In addition, due to charge transfer between the intercalated atoms and bilayer CrI₃, the interlayer coupling transits from antiferromagnetic to ferromagnetic, and the intralayer ferromagnetic coupling is also enhanced. Furthermore, the built-in electric polarization causes a distinct surface magnetization difference, generating a strong magnetoelectric coupling with a coefficient larger than that of Fe, Co, and Ni thin films. Our work paves a practical path for 2D multiferroics, which may have crucial applications in spintronics.

Spontaneous Valley Polarization and Electrical Control of Valley Physics in Single-Layer TcIrGe2S6

The modulation of valley polarization in one single system is of important fundamental and practical importance in quantum information technology. Here, through the first-principles calculations, we identify single-layer TcIrGe₂S₆ as a tantalizing candidate for realizing the modulation of valley polarization. Arising from the combination of inversion symmetry breaking and intrinsic magnetic exchange interaction, single-layer TcIrGe₂S₆ exhibits spontaneous valley polarization. The value of valley polarization in the conduction band is 161 meV, favorable for achieving the intriguing anomalous valley Hall effect. Furthermore, single-layer TcIrGe₂S₆ possesses ferroelectric order. More remarkably, its ferroelectric and valley physics can be strongly coupled, namely, the valley properties can be switched off and on electrically. These findings not only provide a compelling candidate for two-dimensional valleytronic research but also open a new avenue for modulating valley physics.

Discovery of Two-Dimensional Multinary Component Photocatalysts Accelerated by Machine Learning

Searching for novel and high-performance two-dimensional (2D) materials is an important task for photocatalytic applications. Although multinary compounds exhibit more diversity in structure and properties in comparison to binary 2D materials, they are comparatively under-studied. Herein, using a machine-learning (ML) technique and high-throughput screening, we develop an efficient approach to accurately predict 2D multicomponent photocatalysts. Over 4000 monolayers are examined, and 75 multinary compounds are identified for photocatalytic applications. Considering our predictions, we find that the ternary and quaternary compounds A₂P₂X₆ and ABP₂X₆ with A = Cu/Zn/Ge/Ag/Cd, B = Ga/In/Bi, and X = S/Se exhibit superior properties, making them promising candidates for overall water splitting. Thus, our work provides an efficient way to explore novel photocatalysts, which could stimulate further theoretical and experimental investigations on 2D multinary compounds for application in photocatalytic water splitting.