Two-dimensional (2D) semiconducting materials have been studied extensively for their interesting excitonic and optoelectronic properties arising from strong many-body interactions and quantum confinement at 2D limit. Most of these materials have been inorganic, such as transition metal dichalcogenides, phosphorene, etc. Organic semiconductor materials, on the other hand been investigated for their excellent electrical conductivity and low dielectric coefficients for similar applications in the thin film or bulk material phase. The lack of crystallinity in the thin film and bulk phases has led to ambiguity over the excitonic and electronic/optical band gap characteristics. The recent emergence of 2D organic materials has opened a new domain of high crystallinity and controlled morphology, allowing for the study of low-lying excitonic states and optoelectronic properties. They have been demonstrated to have different excitonic properties compared with the Wannier–Mott excitons in inorganic 2D materials. Here we present our recent experimental observations and analysis of 2D organic semiconductor materials. We discuss the role of high-crystalline and morphology-controlled growth of single-crystalline materials and their optoelectronic properties. The report explains the Frenkel (FR) and charge-transfer (CT) excitons and subsequent light emission and absorption properties in organic materials. The true nature of low-lying excitonic states, which arises from the interaction between CT and FR excitons, is experimentally studied and discussed to reveal the electronic band structure. We then discuss the pure FR behaviour we observed in J–type aggregated organic materials leading to coherent superradiant excitonic emissions. The supertransport of excitons within the organic materials, facilitated by their pure FR nature, and the delocalization of excitons over a large number of molecules are also demonstrated. Finally, we discuss the applications and our vision for these organic 2D materials in fast organic light-emitting diodes, high-speed excitonic circuits, quantum computing devices, and other optoelectronic devices.

Material genetic engineering can significantly accelerate the development of new materials. As an important topic in material science and condensed matter physics, the development of metallic glasses (MGs) with specific properties has largely been the result of trial and error since their discovery in 1960. Yet, property design based on the physical parameters of constituent elements of MGs remains a huge challenge owing to the lack of an understanding of the property inheritance from constitute elements to the resultant alloys. In this work, we report the inherent relationships of the yield strength σ_y, Young’s modulus E, and shear Modulus G with the valence electron density. More importantly, we reveal that the electronic density of states (EDOSs) at the Fermi surface (E_F) is an inheritance factor for the physical properties of MGs. The physical properties of MGs are inherited from the specific element with the largest coefficient of electronic specific heat (γ_i), which dominates the value of the EDOS at E_F. This work not only contributes to the understanding of property inheritances but also guides the design of novel MGs with specific properties based on material genetic engineering.

To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e. machine learning potentials (MLPs). One recently developed type of MLP is the deep potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

A close look at Google’s GNoME inorganic materials dataset (Merchant et al 2023 Nature 624 80–85), and 11 things you would like to know.

DFT + U is a widely used treatment in the density functional theory (DFT) to deal with correlated materials that contain open-shell elements, whereby the quantitative and sometimes even qualitative failures of local and semi-local approximations can be corrected without much computational overhead. However, finding appropriate U parameters for a given system and structure is non-trivial and computationally intensive, because the U value has generally a strong chemical and structural dependence. In this work, we address this issue by building a machine learning (ML) model that enables the prediction of material- and structure-specific U values at nearly no computational cost. Using Mn–O system as an example, the ML model is trained by calibrating DFT + U electronic structures with the hybrid functional results of more than 3000 structures. The model allows us to determine an accurate U value (MAE = 0.128 eV, R² = 0.97) for any given Mn–O structure. Further analysis reveals that M–O bond lengths are key local structural properties in determining the U value. This approach of the ML U model is universally applicable, to significantly expand and solidify the use of the DFT + U method.