Advances of Machine Learning in Nanoscale Materials Science
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".
Deadline for manuscript submissions: 10 February 2026 | Viewed by 168
Special Issue Editor
Interests: ferroelectric materials; optoelectronic semiconductors; first-principles calculations; machine learning; machine learning interatomic potentials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Cutting-edge machine learning (ML) techniques such as gradient boosting models (e.g., XGBoost), graph neural networks (e.g., CGCNN, SchNet, and ALIGNN), machine learning interatomic potentials (e.g., BPNN, GAP, M3Gnet, NequIP, MACE, and NEP), and deep learning-based density functional theory Hamiltonians (e.g., DeepH, HamGNN, and DeePTB) are revolutionizing the field of materials science. Leveraging high-quality datasets and advanced algorithms, these methods enable the rapid and reliable prediction of material properties, accelerate the discovery of novel materials through high-throughput screening, facilitate large-scale simulations with unprecedented accuracy and efficiency, and provide mechanistic insights into material behavior across multiple length and time scales. Such breakthroughs have the potential to drastically shorten the materials discovery and optimization cycle, reduce research costs, and unlock new classes of functional materials with extraordinary properties.
This Special Issue aims to present the latest progress in the application of machine learning, machine learning-based interatomic potentials, and data-driven approaches in materials science. Topics include, but are not limited to, ML-assisted property prediction, inverse design, automated experimentation, uncertainty quantification, interpretable AI models, machine learning for electronic structure prediction, and the development of materials databases. We welcome contributions on novel methodologies, benchmarking studies, and applications in areas such as energy materials, ferroelectric materials, functional ceramics, catalysts, polymers, and two-dimensional materials.
We invite leading experts and emerging researchers in the field to share their most recent findings, methodologies, and perspectives. Our goal is to provide a comprehensive and authoritative overview of state-of-the-art advances and future directions at the intersection of artificial intelligence, machine learning, and materials science.
Dr. Gang Tang
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- data-driven materials discovery
- automated experimentation
- inverse design
- materials databases
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