ORCID Profile
0000-0002-2448-8448
Current Organisation
Chinese Academy of Sciences
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Publisher: Wiley
Date: 28-07-2022
Abstract: Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data‐driven scientific research. This transition requires the development of authoritative and up‐to‐date frameworks for data‐driven approaches for material innovation. A critical discussion on the current advances in the data‐driven discovery of materials with a focus on frameworks, machine‐learning algorithms, material‐specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data‐driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data‐intensive strategies and machine‐learning algorithms ii) material databases and related tools and platforms for data generation and management iii) commonly used molecular descriptors used in data‐driven processes. Furthermore, an in‐depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data‐driven paradigms is outlined, and the opportunities and challenges in data‐driven material innovation are highlighted.
Publisher: Wiley
Date: 17-07-2022
Abstract: The development of advanced electronic devices is boosting many aspects of modern technology and industry. The ever‐increasing demand for advanced electrical devices and integrated circuits calls for the design of novel materials, with superior properties for the improvement of working performance. In this review, a detailed overview of the synthesis strategies of 2D metal organic frameworks (MOFs) acquiring growing attention is presented, as a basis for expansion of novel key materials in electrical devices and integrated circuits. A framework of controllable synthesis routes to be implanted in the synthesis strategies of 2D materials and MOFs is described. In short, the synthesis methods of 2D MOFs are summarized and discussed in depth followed by the illustrations of promising applications relating to various electrical devices and integrated circuits. It is concluded by outlining how 2D MOFs can be synthesized in a simpler, highly efficient, low‐cost, and more environmentally friendly way which can open up their applicable opportunities as key materials in advanced electrical devices and integrated circuits, enabling their use in broad aspects of the society.
Publisher: Springer Science and Business Media LLC
Date: 02-03-2023
DOI: 10.1038/S44160-023-00250-5
Abstract: Morphological control with broad tunability is a primary goal for the synthesis of colloidal nanocrystals with unique physicochemical properties. Here we develop a robotic platform as a substitute for trial-and-error synthesis and labour-intensive characterization to achieve this goal. Gold nanocrystals (with strong visible-light absorption) and double-perovskite nanocrystals (with photoluminescence) are selected as typical proof-of-concept nanocrystals for this platform. An initial choice of key synthesis parameters was acquired through data mining of the literature. Automated synthesis and in situ characterization with further ex situ validation was then carried out and controllable synthesis of nanocrystals with the desired morphology was accomplished. To achieve morphology-oriented inverse design, correlations between the morphologies and structure-directing agents are identified by machine-learning models trained on a continuously expanded experimental database. Thus, the developed robotic platform with a data mining–synthesis–inverse design framework is promising in data-driven robotic synthesis of nanocrystals and beyond.
Location: United States of America
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Haitao Zhao.