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Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials.
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In 2016, Hiroaki Kitano proposed that artificial intelligence (AI) will be able to overcome a number of human cognitive limitations that slow down the process of scientific discovery [Kitano 2016 web]. Since then, the odds of AI being awarded the Nobel Prize have been widely discussed, particularly within academic community [Engineering for Research Symposium web 2020]. At the AI Journey 2021 conference, some renowned representatives of four scientific disciplines (physics, mathematics, neurobiology, philosophy) discussed this issue and then co-authored this article [AI 2021 web]. In the first part of our paper, we critically analyze the role of AI technologies in natural science research: how useful they can be for fundamental science, what the potential of AI in natural and exact sciences is, and what principal limitations it has. Another part of our article discusses a counter-question of what science can do for the future research into AI. Today, it is impossible to imagine machine learning without linear algebra, physics of materials, and brain research. All this falls under what is now commonly referred to as AI, a general umbrella term [Russel, Norvig 2021]. Thus, having served the birth of AI once, how can physics, mathematics, and neuroscience serve it today?