Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices

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.

MatGPT: A Vane of Materials Informatics from Past, Present, to Future

Combining materials science, artificial intelligence (AI), physical chemistry, and other disciplines, materials informatics is continuously accelerating the vigorous development of new materials. The emergence of "GPT (Generative Pre-trained Transformer) AI" shows that the scientific research field has entered the era of intelligent civilization with "data" as the basic factor and "algorithm + computing power" as the core productivity. The continuous innovation of AI will impact the cognitive laws and scientific methods, and reconstruct the knowledge and wisdom system. This leads to think more about materials informatics. Here, a comprehensive discussion of AI models and materials infrastructures is provided, and the advances in the discovery and design of new materials are reviewed. With the rise of new research paradigms triggered by "AI for Science", the vane of materials informatics: "MatGPT", is proposed and the technical path planning from the aspects of data, descriptors, generative models, pretraining models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics, is carried out. Finally, the challenges and constraints faced by materials informatics are discussed, in order to achieve a more digital, intelligent, and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists.

Two-dimensional nanomaterials as enhanced surface plasmon resonance sensing platforms: Design perspectives and illustrative applications

Both increasing demand for ultrasensitive detection in the scientific community and significant new breakthroughs in materials science field have inspired and promoted the development of new-generation multifunctional plasmonic sensing platforms by adopting promising plasmonic nanomaterials. Recently, high-quality surface plasmon resonance (SPR) sensors, assisted by two dimensional (2D) nanomaterials including 2D van der Waals (vdWs) materials (such as graphene/graphene oxide, transition metal dichalcogenides (TMDs), phosphorene, antimonene, tellurene, MXenes, and metal oxides), 2D metal-organic frameworks (MOFs), 2D hyperbolic metamaterials (HMMs), and 2D optical metasurfaces, have emerged as a class of novel plasmonic sensing platforms that show unprecedented detection sensitivity and impressive performance. This review of recent progress in 2D nanomaterials-enhanced SPR platforms will highlight their compelling plasmonic enhancement features, working mechanisms, and design methodologies, as well as discuss illustrative practical applications. Hence, it is of great importance to describe the latest research progress in 2D nanomaterials-enhanced SPR sensing cases. In this review, we present some concepts of SPR enhanced by 2D nanomaterials, including the basic principles of SPR, signal modulation approaches, and working enhancement mechanisms for various 2D materials-enhanced SPR systems. In addition, we also demonstrate a detailed categorization of 2D nanomaterials-enhanced SPR sensing platforms and comment on their ability to realize ultrasensitive SPR detection. Finally, we conclude with future perspectives for exploring a new generation of 2D nanomaterials-based sensors.