Machine-Intelligence-Driven High-Throughput Prediction of 2D Charge Density Wave Phases

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.

Massive Monte Carlo simulations-guided interpretable learning of two-dimensional Curie temperature

Monte Carlo (MC) simulation of the classical Heisenberg model has become the de facto tool to estimate the Curie temperature (T _C) of two-dimensional (2D) magnets. As an alternative, here we develop data-driven models for the five most common crystal types, considering the isotropic and anisotropic exchange of up to four nearest neighbors and the single-ion anisotropy. We sample the 20-dimensional Heisenberg spin Hamiltonian and conceive a bisection-based MC technique to simulate a quarter of a million materials for training deep neural networks, which yield testing R ² scores of nearly 0.99. Since 2D magnetism has a natural tendency toward low T _C, learning-from-data is combined with data-from-learning to ensure a nearly uniform final data distribution over a wide range of T _C (10-1,000 K). Global and local analysis of the features confirms the models' interpretability. We also demonstrate that the T _C can be accurately estimated by a purely first-principles-based approach, free from any empirical corrections.

Symmetric Breakage-Induced Semimetallic State: Polymorphism in Ruthenate Nanosheets

Atomic arrangements and their symmetries govern the physical properties of materials, including nanosheets that are low-dimensional nanomaterials. Although they have the same composition, symmetric changes associated with atomic displacements sometimes induce unexpected physical properties. Herein, we report that symmetric breakage induces a semimetallic state in chemically exfoliated ruthenate nanosheets. The atomic arrangements and symmetries are determined by a pair distribution function (PDF); subsequently, the physical properties are discussed using ab initio calculations and resistivity measurements. Ruthenate nanosheets can adopt an electronic structure similar to that of graphene owing to symmetric breakage. We experimentally confirmed the polymorphism in ruthenate nanosheets that highlights the importance of symmetric analysis, even in low-dimensional materials.

Computational Methods for Charge Density Waves in 2D Materials

Two-dimensional (2D) materials that exhibit charge density waves (CDWs)—spontaneous reorganization of their electrons into a periodic modulation—have generated many research endeavors in the hopes of employing their exotic properties for various quantum-based technologies. Early investigations surrounding CDWs were mostly focused on bulk materials. However, applications for quantum devices require few-layer materials to fully utilize the emergent phenomena. The CDW field has greatly expanded over the decades, warranting a focus on the computational efforts surrounding them specifically in 2D materials. In this review, we cover ground in the following relevant theory-driven subtopics for TaS₂ and TaSe₂: summary of general computational techniques and methods, resulting atomic structures, the effect of electron–phonon interaction of the Raman scattering modes, the effects of confinement and dimensionality on the CDW, and we end with a future outlook. Through understanding how the computational methods have enabled incredible advancements in quantum materials, one may anticipate the ever-expanding directions available for continued pursuit as the field brings us through the 21st century.