Unexpected spontaneous symmetry breaking and diverse ferroicity in two-dimensional mono-metal phosphorus chalcogenides.

Mono-metal phosphorus trichalcogenides (MPX) have attracted intensive interest due to their intriguing magnetic properties and potential applications. Generally, single-layer two-dimensional (2D) MPX are believed to be centrosymmetric. However, we discovered that unexpected spontaneous symmetry breaking may occur in some 2D MPX, , vertical P-P dimers move out of the plane and become tilted, leading to the structural stability being enhanced, the inversion symmetry being simultaneously broken, and ferroelectricity or ferroelasticity emerging.

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 CrI to form Cu(CrI) or Ag(CrI).

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

Inspired by experimentally discovering ferromagnetism and ferroelectricity in two-dimensional (2D) CrGeTe and CuInPS with similar geometric structures, respectively, we systematically investigated ferroic properties in a large family of 2D MMGeX (M and M = metal elements, X = S/Se/Te) by combining high-throughput first-principles calculations and the machine learning method. We identified 12 stable 2D multiferroics containing simultaneously ferromagnetic (FM) and ferroelectric (FE) properties and 35 2D ferromagnets without FE polarization. Particularly, the predicted FM Curie temperatures () of eight 2D FM+FE semiconductors are close to or above room temperature.

High-efficient Bayesian active learning method and applications in prediction of two-dimensional functional materials.

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the Bayesian active learning method that combines active learning and high-throughput calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search.

Large family of two-dimensional ferroelectric metals discovered via machine learning.

Ferroelectricity and metallicity are usually believed not to coexist because conducting electrons would screen out static internal electric fields. In 1965, Anderson and Blount proposed the concept of "ferroelectric metal", however, it is only until recently that very rare ferroelectric metals were reported. Here, by combining high-throughput ab initio calculations and data-driven machine learning method with new electronic orbital based descriptors, we systematically investigated a large family (2964) of two-dimensional (2D) bimetal phosphates, and discovered 60 stable ferroelectrics with out-of-plane polarization, including 16 ferroelectric metals and 44 ferroelectric semiconductors that contain seven multiferroics.

Voting Data-Driven Regression Learning for Accelerating Discovery of Advanced Functional Materials and Applications to Two-Dimensional Ferroelectric Materials.

Regression machine learning is widely applied to predict various materials. However, insufficient materials data usually leads to poor performance. Here, we develop a new voting data-driven method that could generally improve the performance of the regression learning model for accurately predicting properties of materials.