Guest Editors
Prof. Carlos A. Lamas
Email: lamas@fisica.unlp.edu.ar
Affiliation: IFLP - Physics Department, University of La Plata, Buenos Aires, 1900, Argentina
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Research Interests: condensed matter, computational physics, machine learning, strongly correlated systems, frustrated magnetism
Prof. Dr. Marcelo Arlego
Email: arlego@fisica.unlp.edu.ar
Affiliation: Departamento de Física, Universidad Nacional de La Plata, La Plata, 1900, Argentina
Homepage:
Research Interests: condensed matter, computational physics, machine learning, strongly correlated systems, frustrated magnetism
Summary
Recent advances in machine learning (ML) are transforming methods used in materials science by enabling rapid prediction, discovery, and design of novel materials with efficiency. As computational power and data availability increase, ML is becoming a powerful tool for accelerating materials research and innovation.
This Special Issue aims to showcase cutting-edge research at the intersection of machine learning and materials science. It invites original contributions showing the transformative potential of machine learning in advancing materials science, from discovery to optimization and deployment. Contributions could include novel algorithms, case studies, benchmarking, or reviews of state-of-the-art techniques. This includes the following topics:
· Machine learning (ML)-based prediction of mechanical, thermal, electronic, and optical properties.
· Generative models (GANs, VAEs, diffusion models) for novel material discovery.
· ML models linking atomic/microstructure to macroscopic properties.
· Interpretable ML models for materials research.
· Deep learning for microscopy (SEM, TEM, AFM) and spectroscopy (XRD, Raman) analysis.
· ML-enhanced molecular dynamics and density functional theory (DFT) simulations.
· Physics-informed neural networks for material simulations.
· Addressing bias and generalizability in materials datasets.
· Transfer learning for small materials datasets.
· Integration of ML with first-principles and multiscale simulations
Keywords
Machine learning in materials science, Deep Learning, Anomaly detection, Convolutional Neural Networks, Computational modeling, Multiscale simulation, Strongly correlated systems, Magnetic materials, Electronic structure
Published Papers
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