Abstract The discovery of graphene, the first two-dimensional (2D) material, has caused an upsurge, as this kind of material revealed a tremendous potential of application in areas such as energy storage, electronics, and gas separation. MXenes are referred to as a family of 2D transition metal carbides, carbonitrides, and nitrides. After the synthesis of Ti3C2 from Ti3AlC2 in 2011, about 30 new compositions have been reported. These materials have been widely discussed, synthesized, and investigated by many research groups, as they have many advantages over traditional 2D materials. This review covers the structures of MXenes, discusses various synthesis routines, analyzes the properties, especially optical and electronic properties, and summarizes their applications and potential, which may give readers an overview of these popular materials.

The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.

Given that robots are being utilized extensively in chemical synthesis research, the potential applications of robots remain to be explored. Along with the remarkable progress of experimental science, circumstances have occurred in which publications were castigated because of irreproducibility, either because of rigorous experimental conditions or because of initial data forgery. Some credit-assignment issues and plagiarism cases also attracted intense attention throughout the community. As a possible solution to authenticity and originality problems, we herein propose a blockchain integrated automatic experiment platform, BiaeP, which attempts to provide solutions for those kinds of problems. As a result of the integration with blockchain, its data irreversibility secures the authenticity and the timestamp helps prove the originality. Two trial experiments are included as examples. We believe the architecture of BiaeP could be widely applicable for future development of scientific research in experimental subjects, such as chemistry, materials science, biology, and so forth.

Applying deep learning methods in materials science research is an important way of solving the time-consuming problems of typical ab initio quantum chemistry methodology, but due to the size of large molecules, large and uncharted fields still exist. Implementing symmetry information can significantly reduce the calculation complexity of structures, as they can be simplified to the minimum symmetric units. Because there are few quantum chemistry databases that include symmetry information, we constructed a new one, named QM-sym, by designing an algorithm to generate 135k organic molecules with the Cnh symmetry composite. Those generated molecules were optimized to a stable state using Gaussian 09. The geometric, electronic, energetic, and thermodynamic properties of the molecules were calculated, including their orbital degeneracy states and orbital symmetry around the HOMO-LUMO. The basic symmetric units were also included. This database p rovides consistent and comprehensive quantum chemical properties for structures with Cnh symmetries. QM-sym can be used as a benchmark for machine learning models in quantum chemistry or as a dataset for training new symmetry-based models.