Guest Editors
Dr. Rocío Rodríguez
Email: rociorg@usal.es
Affiliation: Tidop Research Group, University of Salamanca, Patio de Escuelas 1, E-38008 Salamanca, Spain
Homepage:
Research Interests: neural networks, materials science
Dr. Manuel Curado
Email: manuel.curado@ua.es
Affiliation: Department of Computer Science and Artificial Intelligence, University of Alicante, Campus de San Vicente, Ap. Correos 99, E-03080 Alicante, Spain
Homepage:
Research Interests: neural networks, materials science
Summary
Data science and machine learning are considered the fourth pillar of science capable of supporting all scientific disciplines by correlating them. Deep and machine learning methods are being applied to different stages of materials science, from the creation of new materials to the improvement of existing ones through database studies, from the study of their properties, reverse engineering, automated data analysis. The methods used to perform machine learning models applied to chemistry and materials science are either classical models such as ensembles of decision trees or more modern techniques such as neural networks or sequence models. Many research projects applied to the latest materials focus on neural network methods.
The main objective: to produce a special issue on this topic that brings together the latest and most innovative research on this subject.
New materials and neural networks
- Application of neural networks in materials formation
- Applications of neural networks for the improvement of material properties
- Neural networks applications for sustainability in materials
Keywords
Neural networks, chemistry and materials science
Published Papers
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