Bandgap prediction of two-dimensional materials using machine learning

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.

Predicting band gaps of ABN3 perovskites: an account from machine learning and first-principle DFT studies

The present paper is primarily focused on predicting the band gaps of nitride perovskites from machine learning (ML) models. The ML models have been framed from the feature descriptors and band gap values of 1563 inorganic nitride perovskites having formation energies <-0.026 eV and band gaps ranging from ∼1.0 to 3.1 eV. Four supervised ML models such as multi-layer perceptron (MLP), gradient boosted decision tree (GBDT), support vector regression (SVR) and random forest regression (RFR) have been considered to predict the band gaps of the said systems. The accuracy of each model has been tested from mean absolute error, root-mean-square error and determination coefficient R2 values. The bivariate plots between the predicted and input band gaps of the compounds for both the training and test datasets have also been estimated. Additionally, two ABN3-type nitride perovskites CeBN3 (B = Mo, W) have been selected and their electronic band structures and optoelectronic properties have been studied from density functional theory (DFT) calculations. The band gap values of the said compounds have been estimated from DFT calculations at PBE, HSE06, G0W0@PBE, G0W0@HSE06 level of theories. The present study will be helpful in exploring the ML models in predicting the band gaps of nitride perovskites which in turn may bear potential applications in photovoltaic cells and optical luminescent devices.

Descriptor engineering in machine learning regression of electronic structure properties for 2D materials

We build new material descriptors to predict the band gap and the work function of 2D materials by tree-based machine-learning models. The descriptor's construction is based on vectorizing property matrices and on empirical property function, leading to mixing features that require low-resource computations. Combined with database-based features, the mixing features significantly improve the training and prediction of the models. We find R[Formula: see text] greater than 0.9 and mean absolute errors (MAE) smaller than 0.23 eV both for the training and prediction. The highest R[Formula: see text] of 0.95, 0.98 and the smallest MAE of 0.16 eV and 0.10 eV were obtained by using extreme gradient boosting for the bandgap and work-function predictions, respectively. These metrics were greatly improved as compared to those of database features-based predictions. We also find that the hybrid features slightly reduce the overfitting despite a small scale of the dataset. The relevance of the descriptor-based method was assessed by predicting and comparing the electronic properties of several 2D materials belonging to new classes (oxides, nitrides, carbides) with those of conventional computations. Our work provides a guideline to efficiently engineer descriptors by using vectorized property matrices and hybrid features for predicting 2D materials properties via ensemble models.

Opportunities and challenges for 2D heterostructures in battery applications: a computational perspective

With an increasing demand for large-scale energy storage systems, there is a need for novel electrode materials to store energy in batteries efficiently. 2D materials are promising as electrode materials for battery applications. Despite their excellent properties, none of the available single-phase 2D materials offers a combination of properties required for maximizing energy density, power density, and cycle life. This article discusses how stacking distinct 2D materials into a 2D heterostructure may open up new possibilities for battery electrodes, combining favourable characteristics and overcoming the drawbacks of constituent 2D layers. Computational studies are crucial to advancing this field rapidly with first-principles simulations of various 2D heterostructures forming the basis for such investigations that offer insights into processes that are hard to determine otherwise. We present a perspective on the current methodology, along with a review of the known 2D heterostructures as anodes and their potential for Li and Na-ion battery applications. 2D heterostructures showcase excellent tunability with different compositions. However, each of them has distinct properties, with its own set of challenges and opportunities for application in batteries. We highlight the current status and prospects to stimulate research into designing new 2D heterostructures for battery applications.