SS06: Neural Networks in Chemistry and Material Characterization
Ruxandra Stoean
University of Craiova, Romania
Patricio García Báez
Universidad de La Laguna, Spain
Carmen Paz Suárez Araujo
Universidad de Las Palmas de Gran Canaria, Spain
Abstract
The application of neural networks and deep learning in the fields of chemistry and materials science is an emergent direction towards the comprehension of the chemical interactions, material structure and matter dynamics through machine learning. The practical applications to contingent domains, such as archaeology and civil engineering, add to the picture of AI support tools.
The aim of this special session is to bring together computer scientists working in applying neural network architectures to these areas, as well as chemistry and materials science engineers interested in the assistance that machine/deep learning can give in their complex tasks.
The topics targeted by this special session are as follows, but not limited to:
- Neural networks for the identification of chemical elements and mixtures from spectroscopy
- Neural networks for corrosion analysis
- Neural networks for material characterization
- Graph neural networks for chemistry and materials science
- Computational chemistry with neural networks
- Deep learning for image processing in cultural heritage
- Deep learning for civil engineering
Organizers
Dr. Ruxandra Stoean is Associate Professor at the University of Craiova, Romania, and Principal Investigator at the Romanian Institute of Science and Technology, Cluj-Napoca, Romania. She holds a PhD in computer science, focused on optimization through evolutionary computation. Her current research interests involve the development of deep learning models for images and signals, with applications in medicine, engineering and cultural heritage. She serves as Academic Editor for the Plos One journal.
Dr. Patricio García Báez is Assistant Professor in Computer Science and Artificial Intelligence at the Universidad de La Laguna, Spain. He teaches courses related to Artificial Intelligence and Artificial Neural Networks. His research is focussed on the field of Artificial Neural Networks which has led him to publish several articles in national and international level and contribute to the participation and organization of various conferences and seminars. The focus areas of his works are the design of new neural architectures and application in the field of clinical diagnosis and the processing of biological and environmental signals.
Prof. Carmen Paz Suárez Araujo is Professor of Computer Sciences and Artificial Intelligence at the Universidad de las Palmas de Gran Canaria (ULPGC). She is BS & MS in Physics and PhD in Computer Sciences. She has been Director of Intelligent Computing, Perception and Big Data Research Group of ULPGC and currently she is Head of Computational Neuroscience Research Division at the Institute of Cybernetics Science and Technology of the ULPGC. She has been Director of PhD. Program of Neural Computing in Natural and Artificial Systems of the same University. She has been Vice-Rector of the ULPGC, Vice-Dean of the Faculty of Computer Sciences of this University for many years.
She has been Invited Professor in a broad range of foreign Universities, around 30, with longer research stays in several of them: the University of Florida, Universidade de Lisboa, Comenius University, visiting professor during a year, at the University of Technology Sydney (UTS) and University of Arizona.
She has an extensive research experience focused in the knowledge of the brain structure and function and designing models and computational systems with some brain capacities and intelligent systems for decision-making, with more than 140 scientific articles and book chapters, and more than 150 contributions to international and national congresses. Her research lines are • Natural and Artificial Neural Networks. Design of New Neural Architectures; •Application of Neural Computation in Clinical and Environmental Domains, Biomedicine, Neuroinformatics and Bioinformatics; •Computational Neuroscience and Cognitive Computation: Neural communication models and learning.
The mid-to-long term scientific-technical interests and objectives of the research agenda are focused on improving intelligent computing models to analyse clinical data and to reach accurate and reliable early diagnosis of AD and differential diagnosis of dementia, and also effective computational tools for translational medicine and personalized medicine, in the scope of brain disease & the COVID-19. Other important objective is to determine the big cross-relation between intelligent computing and Chemistry and Material Science.
Her research work has been awarded for Spanish and international institutions (Spain Real Doctors Academy, The CAN of Sciences 2020, amongst others).