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Artificial Intelligence and Machine Learning for Material Design, Discovery, and Optimization

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 1383

Special Issue Editors


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Guest Editor
Materials and Failure Modeling, Sandia National Laboratories, Albuquerque, NM, USA
Interests: constitutive modeling; multifunctional materials; physics informed machine learning; additive manufacturing

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Guest Editor
College of Engineering, Oregon State University, Corvallis, OR, USA
Interests: smart/active materials; additive manufacturing; 4D printing; artificial intelligence process optimization

Special Issue Information

Dear Colleagues,

This Special Issue focuses on recent advancements in the use of artificial intelligence (AI) and machine learning (ML) techniques in the broad field of material science. The rapid advancement of AI/ML and their subsequent adoption by the scientific community has seen an explosion in research in this field. We welcome contributions focusing on the use of AI/ML techniques for computational, experimental, and/or theoretical purposes. We seek manuscripts that focus on the successful deployment of technologies in application topics including, but not limited to, material discovery, high throughput experimentation and data analysis, the optimization of material processing, multiscale modeling and simulation, optimal experimental design, etc. Special interest will be given to those manuscripts which leverage AI/ML techniques beyond pure data-driven techniques and which create “black-box” discoveries. For example, the successful combination of AI/ML for the discovery of fundamental physical laws will be of particular interest.

Dr. Craig Hamel
Dr. Devin J. Roach
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart materials
  • polymers
  • additive manufacturing
  • recyclable materials
  • physics-informed machine learning

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Published Papers (1 paper)

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Research

13 pages, 1213 KiB  
Article
Prediction of Resilient Modulus Value of Cohesive and Non-Cohesive Soils Using Artificial Neural Network
by Andrzej Głuchowski
Materials 2024, 17(21), 5200; https://doi.org/10.3390/ma17215200 - 25 Oct 2024
Viewed by 1031
Abstract
This paper investigates the application of Artificial Neural Networks (ANNs) for predicting the resilient modulus (Mr) of subgrade and subbase soils, which is a critical parameter in pavement design. Utilizing a dataset of 1683 Mr observations, the ANN model [...] Read more.
This paper investigates the application of Artificial Neural Networks (ANNs) for predicting the resilient modulus (Mr) of subgrade and subbase soils, which is a critical parameter in pavement design. Utilizing a dataset of 1683 Mr observations, the ANN model incorporates eight input variables, including soil gradation, plasticity, and stress conditions. The model was optimized using a quasi-Newton method, achieving high predictive accuracy, with a coefficient of determination (R2) of 0.9613 and low error rates for both selection and testing datasets. To further enhance model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, revealing the significant influence of specific input parameters, such as saturation ratio, plasticity index and soil gradation, on Mr predictions. This study underscores the potential of ANNs as a practical tool for estimating resilient modulus, offering a reliable alternative to conventional laboratory testing methods. The findings suggest that integrating ANNs into pavement design processes can lead to more accurate predictions of pavement performance, ultimately supporting the development of more efficient and durable road structures. Full article
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