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Applied Sciences
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Artificial Intelligence in the Innovation of Materials Science and Engineering
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Artificial Intelligence in the Innovation of Materials Science and Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 6415

Special Issue Editors

Prof. Dr. Yuancheng Cao

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Guest Editor
School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: alloy and compounds materials; lithium ions batteries; machine learning in materials
Prof. Dr. Songfeng Lu

E-Mail Website
Guest Editor
School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: machine learning; data analysis

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) has received widespread attention due to its potential to increase automation and accelerate productivity over the last few decades. A large amount of training data with improved computing power and advanced deep learning algorithms are conducive to the wide application of artificial intelligence, including material research. The traditional trial-and-error method is inefficient and time-consuming to the material innovations. AI-based innovations in energy storage materials, especially machine learning, can accelerate the process by learning rules from datasets and building models to predict. This is completely different from computational chemistry. This Special Issue is focused on the application of AI in material innovation; papers need not be limited to the material design, performance prediction, and synthesis. All submissions are welcome.

Prof. Dr. Yuancheng Cao
Prof. Dr. Songfeng Lu
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. Applied Sciences 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 2400 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

  • Artificial Intelligence (AI)
  • machine learning
  • materials data
  • materials innovation

Published Papers (2 papers)

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Research

17 pages, 3736 KiB  
Article
Quantitative Description for Sand Void Fabric with the Principle of Stereology
by Xuefeng Li, Zhigang Ma and Fanchao Meng
Appl. Sci. 2021, 11(23), 11158; https://doi.org/10.3390/app112311158 - 24 Nov 2021
Cited by 1 | Viewed by 1488
Abstract
Based on the principle of stereology to describe void fabric, the fabric tensor is redefined by the idea of normalization, and a novel quantitative description method for the orthotropic fabric of granular materials is presented. The scan line is described by two independent [...] Read more.
Based on the principle of stereology to describe void fabric, the fabric tensor is redefined by the idea of normalization, and a novel quantitative description method for the orthotropic fabric of granular materials is presented. The scan line is described by two independent angles in the stereo space, and the projection of the scan line on three orthogonal planes is used to determine the plane tensor. The second-order plane tensor can be described equivalently by two invariants, which describe the degree and direction of anisotropy of the material, respectively. In the three-dimensional orthogonal space, there are three measurable amplitude parameters on the three orthogonal planes. Due to the normalized definition of tensor in this paper, there are only two independent variations of the three amplitude parameters, and any two amplitude parameters can be used to derive the three-dimensional orthotropic fabric tensor. Therefore, the same orthorhombic anisotropy structure can be described by three fabrics, which enriches the theoretical description of orthotropy greatly. As the geometric relationship of the stereoscopic space scan line changes, the three sets of orthotropic fabrics degenerate into different forms of transversely isotropic and isotropic fabrics naturally and have a clear physical meaning. The novel fabric tensor is quantitatively determined based on mathematical probability and statistics. The discrete distribution of voids in space is projected as a scalar measurable parameter on a plane. This parameter is related to the macroscopic constitutive relationship directly and can be used to describe the effect of microscopic voids on the macroscopic phenomenon of materials. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Innovation of Materials Science and Engineering)
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13 pages, 1776 KiB  
Article
Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
by Danpeng Cheng, Wuxin Sha, Linna Wang, Shun Tang, Aijun Ma, Yongwei Chen, Huawei Wang, Ping Lou, Songfeng Lu and Yuan-Cheng Cao
Appl. Sci. 2021, 11(10), 4671; https://doi.org/10.3390/app11104671 - 20 May 2021
Cited by 14 | Viewed by 4094
Abstract
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of [...] Read more.
Battery lifetime prediction is a promising direction for the development of next-generation smart energy storage systems. However, complicated degradation mechanisms, different assembly processes, and various operation conditions of the batteries bring tremendous challenges to battery life prediction. In this work, charge/discharge data of 12 solid-state lithium polymer batteries were collected with cycle lives ranging from 71 to 213 cycles. The remaining useful life of these batteries was predicted by using a machine learning algorithm, called symbolic regression. After populations of breed, mutation, and evolution training, the test accuracy of the quantitative prediction of cycle life reached 87.9%. This study shows the great prospect of a data-driven machine learning algorithm in the prediction of solid-state battery lifetimes, and it provides a new approach for the batch classification, echelon utilization, and recycling of batteries. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Innovation of Materials Science and Engineering)
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