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Machine Learning for Materials Design
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Machine Learning for Materials Design

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 2457

Special Issue Editors

Prof. Dr. Wencong Lu

E-Mail Website
Guest Editor
Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
Interests: materials informatics; material data mining; material design; machine learning
Dr. Minjie Li

E-Mail Website
Guest Editor
Department of Chemistry, College of Sciences, Shanghai University, Shanghai, China
Interests: material design and performance optimization research

Special Issue Information

Dear Colleagues,

The integration of machine learning with material design is revolutionizing the way new materials are discovered, characterized, and optimized. Traditional approaches to material design often involve costly and time-consuming experimental and computational methods. However, ML offers powerful tools to accelerate these processes by predicting material properties, discovering new materials, optimizing compositions, and understanding complex material behaviors. This Special Issue seeks to gather cutting-edge research that utilizes ML to address challenges and unlock new potentials in material design.

We invite submissions of original research articles, reviews, and case studies that cover, but are not limited to, the following topics:

  1. Machine learning algorithms for material discovery:
  • Applications of supervised, unsupervised, reinforcement learning, and deep learning in material design.
  • Development and validation of ML models for discovering new materials and/or predicting material properties and behaviors.
  1. Data-driven material design:
  • High-throughput screening and optimization of materials using ML.
  • Integration of computational and experimental data for ML model training and prediction.
  1. Inverse design and optimization:
  • Using ML for inverse design of materials with target properties.
  • Optimization of material compositions and processing conditions.
  • Searching novel materials with optimal properties.
  1. Big data and materials informatics:
  • Handling and analysis of large-scale material datasets.
  • Development of materials databases and their utilization in ML.
  1. Predictive modeling and simulations:
  • Accelerating molecular dynamics and finite element simulations with ML.
  • ML-enhanced multiscale modeling of material systems.
  1. Case studies and applications:
  • Successful real-world applications of ML in materials design.
  • Cross-disciplinary studies involving ML and materials science.

Prof. Dr. Wencong Lu
Dr. Minjie Li
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

  • machine learning
  • algorithms
  • materials design
  • deep learning
  • materials predication
  • materials optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

14 pages, 6320 KiB  
Article
Deep Reinforcement Learning-Guided Inverse Design of Transparent Heat Mirror Film for Broadband Spectral Selectivity
by Zhi Zeng, Haining Ji, Tianjian Xiao, Peng Long, Bin Liu, Shisong Jin and Yuxin Cao
Materials 2025, 18(12), 2677; https://doi.org/10.3390/ma18122677 - 6 Jun 2025
Viewed by 448
Abstract
With the increasing energy consumption of buildings, transparent heat mirror films have been widely used in building windows to enhance energy efficiency owing to their excellent spectrally selective properties. Previous studies have typically focused on spectral selectivity in the visible and near-infrared bands, [...] Read more.
With the increasing energy consumption of buildings, transparent heat mirror films have been widely used in building windows to enhance energy efficiency owing to their excellent spectrally selective properties. Previous studies have typically focused on spectral selectivity in the visible and near-infrared bands, as well as single-parameter optimization of film materials or thickness, without fully exploring the performance potential of the films. To address the limitations of traditional design methods, this paper proposes a deep reinforcement learning-based approach that employs an adaptive strategy network to optimize the thin-film material system and layer thickness parameters simultaneously. Through inverse design, a Ta2O5/Ag/Ta2O5/Ag/Ta2O5 (42 nm/22 nm/79 nm/22 nm/40 nm) thin-film structure with broadband spectral selectivity was obtained. The film exhibited an average reflectance of 75.5% in the ultraviolet band and 93.2% in the near-infrared band while maintaining an average visible transmittance of 87.0% and a mid- to far-infrared emissivity as low as 1.7%. Additionally, the film maintained excellent optical performance over a wide range of incident angles, making it suitable for use in complex lighting environments. Building energy simulations indicate that the film achieves a maximum energy-saving rate of 17.93% under the hot climatic conditions of Changsha and 16.81% in Guangzhou, demonstrating that the designed transparent heat mirror film provides a viable approach to reducing building energy consumption and holds significant potential for practical applications. Full article
(This article belongs to the Special Issue Machine Learning for Materials Design)
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15 pages, 2377 KiB  
Article
Data-Mining-Aided-Material Design of Doped LaMnO3 Perovskites with Higher Curie Temperature
by Lumin Tian, Wentan Wang, Xiaobo Ji, Zhibin Xu, Wenyan Zhou and Wencong Lu
Materials 2025, 18(11), 2437; https://doi.org/10.3390/ma18112437 - 23 May 2025
Viewed by 292
Abstract
The Curie temperature (Tc) of LaMnO3-based perovskites is one of the most important properties associated with their magnetic and spintronic applications. The search for new perovskites with even higher Tc is a challenging problem in material design. Through the systematic optimization [...] Read more.
The Curie temperature (Tc) of LaMnO3-based perovskites is one of the most important properties associated with their magnetic and spintronic applications. The search for new perovskites with even higher Tc is a challenging problem in material design. Through the systematic optimization of support vector regression (SVR) architecture, we establish a predictive framework for determining the Curie temperature (Tc) of doped LaMnO3 perovskites, leveraging fundamental atomic descriptors. The correlation coefficient (R) between the predicted and experimental Curie temperatures demonstrated high values of 0.9111 when evaluated through the leave-one-out cross-validation (LOOCV) approach, while maintaining a robust correlation of 0.8385 on the independent test set. The subsequent high-throughput screening of perovskite compounds exhibiting higher Curie temperatures was implemented via our online computation platform for materials data mining (OCPMDM), enabling the rapid identification of candidate materials through systematic screening protocols. The findings demonstrate that machine learning exhibits significant efficacy and cost-effectiveness in identifying lanthanum manganite perovskites with elevated Tc, as validated through comparative computational and empirical analyses. Furthermore, a web-based computational infrastructure is implemented for the global dissemination of the predictive framework, enabling the open-access deployment of the validated machine learning model. Full article
(This article belongs to the Special Issue Machine Learning for Materials Design)
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10 pages, 2274 KiB  
Communication
Effect of Al Content and Local Chemical Order on the Stacking Fault Energy in Ti–V–Zr–Nb–Al High-Entropy Alloys Based on First Principles
by Mengyao Chen, Xiaowen Yang, Xinpeng Zhao, Cheng Wen and Haiyou Huang
Materials 2025, 18(9), 2053; https://doi.org/10.3390/ma18092053 - 30 Apr 2025
Viewed by 399
Abstract
As a promising candidate for next-generation aviation structural materials, lightweight refractory high entropy alloys (HEAs) exhibit high strength, low density, and excellent high-temperature performance. In this study, we investigated the influence of local chemical ordering on the properties of Ti–V–Zr–Nb–Al HEAs using Monte [...] Read more.
As a promising candidate for next-generation aviation structural materials, lightweight refractory high entropy alloys (HEAs) exhibit high strength, low density, and excellent high-temperature performance. In this study, we investigated the influence of local chemical ordering on the properties of Ti–V–Zr–Nb–Al HEAs using Monte Carlo (MC) simulations based on density functional theory (DFT) calculations. We established that the chemical short-range ordering (SRO) in Ti–V–Zr–Nb–Al HEAs increases with the Al content, resulting in a gradual increase in stacking fault energy (SFE). This theoretical investigation suggests that SRO can be utilized to tailor the performance of HEAs, thereby providing guidance for the scientific design of macroscopic mechanical properties. Full article
(This article belongs to the Special Issue Machine Learning for Materials Design)
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17 pages, 11327 KiB  
Article
GCN-Based Framework for Materials Screening and Phase Identification
by Zhenkai Qin, Qining Luo, Weiqi Qin, Xiaolong Chen, Hongfeng Zhang and Cora Un In Wong
Materials 2025, 18(5), 959; https://doi.org/10.3390/ma18050959 - 21 Feb 2025
Viewed by 625
Abstract
This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, [...] Read more.
This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.990 and a recall of 0.872. This performance is attained with minimal hyperparameter tuning, making it scalable for large-scale material discovery applications. Data augmentation, including synthetic data generation and noise injection, enhances the model’s robustness by simulating real-world experimental variations. However, the model’s reliance on synthetic data and the computational cost of graph construction and inference remain limitations. Future work will focus on integrating real experimental data, optimizing computational efficiency, and exploring lightweight architectures to improve scalability for high-throughput applications. Full article
(This article belongs to the Special Issue Machine Learning for Materials Design)
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