Accelerated design of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:mi>M</mml:mi><mml:mi>T</mml:mi><mml:mi>X</mml:mi></mml:mrow></mml:math> alloys with targeted magnetostructural properties through interpretable machine learning

Hartnett, Timothy Q.; Sharma, Vaibhav; Garg, Sunidhi; Barua, Radhika; Balachandran, Prasanna V.

doi:10.1016/j.actamat.2022.117891

Cited by 11 publications

(1 citation statement)

References 63 publications

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“…[33,189,190] It can be seen that with powerful data analysis capabilities and low research costs, AI has been widely used in property prediction, material structure search, and new material design. At the application level, AI not only has great advantages over traditional calculation methods in different fields, but also has more and more achievements in different material modeling tasks, such as electronic structure, [51,[191][192][193] ionic conductivity, [83,94,194] stability, [195][196][197][198] mechanical property, [199][200][201] optical property, [202][203][204] magnetism, [205,206] [53] Copyright 2021, The Authors, published by Springer Nature.…”

Section: Other Explorationsmentioning

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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