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 March 2025 | Viewed by 158
Special Issue Editors
Interests: materials informatics; material data mining; material design; machine learning
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:
- 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.
- 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.
- 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.
- Big data and materials informatics:
- Handling and analysis of large-scale material datasets.
- Development of materials databases and their utilization in ML.
- Predictive modeling and simulations:
- Accelerating molecular dynamics and finite element simulations with ML.
- ML-enhanced multiscale modeling of material systems.
- 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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