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Artificial Intelligence and Advanced Materials
arXiv - PHYS - Materials Science Pub Date : 2022-09-23 , DOI: arxiv-2209.11618
Cefe López

Artificial intelligence is gaining strength and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms. Future materials scientists will profit from understanding how machine learning can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by compu-tation to account for the origins, procedures and applications of artificial intelligence. Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential. The materials and systems used to implement artificial intelligence with electric charges are finding serious competition from other information carrying and processing agents. The impact these techniques are having on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materi-als and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom as exemplified by the power to discover unheard of mate-rials or physical laws buried in data.

中文翻译:

人工智能与先进材料

人工智能正在获得力量,材料科学可以为人工智能做出贡献并从中受益。在同步的进步竞赛中,借助机器学习技术,可以设计和优化新材料、系统和工艺,并将这些进步转化为创新的计算平台。未来的材料科学家将从了解机器学习如何促进先进材料的概念中获益。这篇综述涵盖了计算的各个方面,从基础到所采取的方向和计算产生的影响,以解释人工智能的起源、程序和应用。回顾机器学习及其方法,以提供有关其实施及其潜力的基本知识。用于实现带电荷人工智能的材料和系统正面临来自其他信息携带和处理代理的激烈竞争。这些技术对新的先进材料的诞生产生了如此深刻的影响,以至于正在开发一种新的范式,在这种范式中,正在挖掘隐性知识来构思材料和系统的功能,而不是寻找已发现材料的应用。这种趋势能走多远很难理解,例如发现隐藏在数据中的闻所未闻的材料或物理定律的能力。这些技术对新的先进材料的诞生产生了如此深刻的影响,以至于正在开发一种新的范式,在这种范式中,正在挖掘隐性知识来构思材料和系统的功能,而不是寻找已发现材料的应用。这种趋势能走多远很难理解,例如发现隐藏在数据中的闻所未闻的材料或物理定律的能力。这些技术对新的先进材料的诞生产生了如此深刻的影响,以至于正在开发一种新的范式,在这种范式中,正在挖掘隐性知识来构思材料和系统的功能,而不是寻找已发现材料的应用。这种趋势能走多远很难理解,例如发现隐藏在数据中的闻所未闻的材料或物理定律的能力。
更新日期:2022-09-26
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