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Innovation in Materials: Key Steps for Algorithm Selection in Predicting Mechanical Characteristics through Machine Learning
Galo Cerda-Mejía
1, 2 ,
Karel Diéguez Santana
* 3 ,
Juan M. Ruso
2
1  Universidad Regional Amazónica Ikiam, Parroquia Muyuna km 7 vía Alto Tena, 150150, Tena-Napo, Ecuador
2  Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
3  Universidad Regional Amazónica IKIAM
Academic Editor: Humbert G. Díaz
Published: 23 December 2023 by MDPI in MOL2NET'23, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 9th ed. congress CHEMBIO.MOL-09: Org. Chem., Med. Chem., Mol. Biol., & Pharm. Industry Congress, Paris, France-Fargo, USA, 2023.
Abstract:

The central importance of materials in society and their relationship with various properties is highlighted. The growing relevance of artificial intelligence (AI), especially machine learning (ML) and deep learning algorithms, in mechanical engineering and materials science is emphasized. The ability of AI to predict features and create innovative materials is highlighted. Furthermore, the crucial steps for applying ML in materials innovation are described, from data collection and cleaning to algorithm selection and optimization, emphasizing the importance of understanding the nature of data and model validation. Finally, a comprehensive overview of the integration of AI and ML in materials research is provided, highlighting their fundamental role in the optimization and prediction of mechanical properties.

Keywords: Algorithm Selection; Data Collection; Data Representation; Materials science; Model Optimization
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Comments on this paper
Maider Baltasar Marchueta
4 January 2024
Dear authors thank you for your support to the conference.

Now we closed the publication phase and launched the post-publication phase of the conference. REVIEWWWERS Brainstorming Workshop is now open until January 25th. MOL2NET committee, authors, and social media followers worldwide are invited to post questions/answers and comments about papers. Please kindly post your public Answers (A) to the following questions in order to promote interchange of scientific ideas.

My Questions (Q) to you:
Q1. How do you foresee the ongoing advancements in AI, particularly ML and deep learning algorithms, influencing the future landscape of mechanical engineering and materials science,
Q2. and what specific breakthroughs or innovations do you anticipate in the creation of new materials?

Dear author thanks in advance for your kind support answering the questions.

Now, the conference publication phase is close but you can answer here directly as a post-publication comment.

Last but not least, please become a verified REVIEWWWER of our conference by making questions to other papers in different Mol2Net congresses. Commenting steps: login, go to papers list, select paper, write comment, click post comment.

Happy new year 2024.

Thanks and kind regards,
CHEMBIOMOL Committee Assistant, Maider Baltasar Marchueta.
Galo Cerda Mejía
13 January 2024
Advances in artificial intelligence (AI), especially machine learning and deep learning algorithms, are having a significant impact on mechanical engineering and materials science, and are expected to continue influencing the future of these disciplines in several ways. In Mechanical Engineering: AI-assisted design, Predictive maintenance, Advanced simulations, Intelligent robotics. And in Materials Science: Materials discovery, Custom materials design, Large-scale data analysis, Manufacturing process optimization. In terms of specific advances in the creation of new materials, innovations are expected in areas such as smart materials (that respond to external stimuli), advanced composite materials, nanomaterials with unique properties, and biomaterials for medical applications. Combining AI with experimental and theoretical techniques will enable the discovery and design of materials with tailored properties and revolutionize the way materials are developed and used in engineering.


 
 
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