TITLE:
Artificial neural networks: a deeper insight into ionic liquids.
DIRECTORS:
Kenneth R. Seddon y José S. Torrecilla.
OBJECTIVES:
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Detailed description of the main features and progress within the ionic liquid field.
Explanation of the principal characteristics of artificial neural networks.
Design of computerized models based on artificial neural networks using
experimental data.
Comprehension of the main differences between the current models in the ionic
liquid field.
SUMMARY:
The course will offer a general view of the combination of two interesting research
fields within the international scientific community. First of all, ionic liquids represent a
rapidly expanding chemical market with great interest in research due to the great
number of new synthesized compounds and novel applications. On the other hand,
computerized artificial intelligence embodies a group of tools that are suitable for
supporting a wide variety of research areas.
The novelty and high progress rate of the ionic liquid field implies a great amount of
difficulty to understand their chemical behaviour and calculate their physicochemical
properties. Both these aspects heavily limit the design of useful new ionic liquid-related
applications, and, therefore, it should be resolved. This is the reason why many research
groups which work with ionic liquids measure their physicochemical properties and
their variation with the surrounding conditions. Despite of all this work, there is still a
lack of knowledge in this regard that can be avoided to a great extent with mathematical
tools. To do so, mathematical models which can accurately define the behaviour of
ionic liquids, as well as estimate their properties, are required. And going a bit further,
these models could be trained to estimate properties for hypothetical ionic liquids, yet to
be synthesised, to assist in their chemical design.
Currently, there are predictive models based on mathematical algorithms that are
employed to describe the nature of various compounds (covalent, salts, and others).
These models can be applied to ionic liquids in some cases, offering acceptable results.
Nevertheless, the main problem behind these systems is that they are not designed
purposely for ionic liquids. On the other hand, we count with models that can be
designed specifically for ionic liquids, and that in some situations use experimental
data. This is the case of artificial neural networks. Given the excellent results attained
when combining ionic liquids with neural networks, these models have been developed
in numerous fields and to fulfil dissimilar tasks such as property estimation,
contamination assessment, ionic liquid design, chemometric tool creation, and so on.
During this course, the reason why applications involving ionic liquids are increasing on
a daily basis will be explained, together with the main differences they have versus
other chemical compounds. Additionally, the mathematical tools currently employed
will be studied and compared with intelligent models. The main characteristics and
advantages that artificial neural networks provide against other empirical algorithms
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will also be analysed. Finally, to get the most out of the neural network lectures, the
students will design their own artificial neural network utilizing a real ionic liquidrelated database. This way, the main goal of the course will be fulfilled, which is
learning how to design an artificial neural network for an ionic liquid application.
PROGRAMME:
1. Ionic liquids (3 - 4 hours)
2. An introduction about artificial intelligence (1 hour)
I.
Birth and development
3. Artificial neural networks (5 hours)
I.
Biological-based origin and historical background
II.
Definition and types of artificial neural networks
III.
Basic components of Artificial Neural Network
IV.
Learning processes
1. Supervised
a) Back-propagation algorithm
b) Multilayer Perceptron.
c) Recurrent neural network
d) Examples
2. Non-supervised
a) Competitive learning
b) Hebb learning
c) Self-organizing maps
d) Examples
V. Practical advice for the correct application of neural networks
VI.
Strengths and weaknesses of neural network models
VII.
Comparison between parametric and non-parametric models
4. Design of computerized models of ionic liquid-related data (6 hours)
I.
Concentration estimation
II.
Ionic liquid customization
III.
Ionic liquid mixtures: physicochemical property estimation
IV.
Other examples
DURATION OF THE COURSE:
16 hours.
DATES:
7th and 8th April 2014.
SCHEDULE:
From 9:00am to 2:00pm, and from 4:00pm to 6:00pm.
PRICE:
CLASSROOMS:
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Events hall and Thesis hall (Library Building), Facultad de Ciencias Químicas,
Universidad Complutense de Madrid.
NUMBER OF STUDENTS:
From 40 to 50.
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