Artificial Intelligence and Advanced Materials

Искусственный интеллект и передовые материалы
Cefe López
2022-12-23

advanced materialsartificial intelligencemachine learningmaterials discoverymaterials science
Artificial intelligence (AI) 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 (ML) techniques, and such progress can be turned into innovative computing platforms. Future materials scientists will profit from understanding how ML can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by computation to account for the origins, procedures, and applications of AI. ML and its methods are reviewed to provide basic knowledge of its implementation and its potential. The materials and systems used to implement AI with electric charges are finding serious competition from other information-carrying and processing agents. The impact these techniques have on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materials 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 materials or physical laws buried in data.
1
Data-driven methods may uncover previously unknown materials and physical laws embedded in large datasets.
2
Machine learning is driving a paradigm shift toward designing materials and systems for desired functions rather than identifying applications for existing materials.
3
Machine-learning techniques enable the design and optimization of new materials, systems, and processes, while advanced materials can enable innovative computing platforms.
4
Materials and systems based on electric charges face increasing competition from alternative information-carrying and processing agents.
5
The review connects AI computation fundamentals, development directions, and impacts with materials-science applications and future research opportunities.

AI-driven advanced materials, systems, and processes

The use of machine learning to conceive, optimize, and discover advanced materials, systems, and physical laws

Publication Details
Publication Date
2022-12-23
Journal
Advanced Materials
Publisher
ISSN
0935-9648
Cited by
118
Access Type
Author Information
Authors
Cefe López
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat →
Make a presentation
100%