Multi-parameter study and genetic algorithm integrated optimization for a nanofluid-based photovoltaic/thermal system

Ju, Xinyu; Liu, Huawei; Pei, Maoqing; Li, Wenzhi; Lin, Jianqing; Liu, Dongxue; Jü, Xing; Xu, Chao

doi:10.1016/j.energy.2022.126528

Cited by 11 publications

(1 citation statement)

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“…Materials make up the world around us that all fields are inseparable from the application of materials, from housing construction to electronic devices, from vehicles to medical devices. [1][2][3][4] A wide variety of materials, such as metals, plastics, ceramics, and semiconductors, have their own unique physical, chemical, and mechanical properties to meet the application needs of different scenarios. Metals usually have good electronic and thermal conductivity, [5,6] plastics have light weight and plasticity, [7] ceramics have high temperature stability and corrosion resistance, [8,9] and semiconductors play a key role in electronic and photovoltaic devices due to good thermal and photosensitive properties.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Chen,

Tao

et al. 2023

Advanced Materials

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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 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 us to think more about materials informatics, both in terms of opportunities and challenges. In this review, we provide a comprehensive discussion of AI models, materials infrastructures, and review the current advances in the discovery and design of new materials. With the rise of new research paradigms triggered by “AI for Science”, we propose the vane of materials informatics: “MatGPT”, and carry out the technical path planning from the aspects of data, descriptors, generative models, pre‐training models, directed design models, collaborative training, experimental robots, as well as the efforts and preparations needed to develop a new generation of materials informatics. Finally, we discuss the challenges and constraints faced by materials informatics, in order to achieve a more digital, intelligent and automated construction of materials informatics with the joint efforts of more interdisciplinary scientists. This article is protected by copyright. All rights reserved

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