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Theme: SynTech CDT

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6 matching courses


Chemistry: CDT Computational Parametrization new Wed 20 Nov 2019   13:00 [Full]

This course will introduce students to the central question of how to encode molecules and molecular properties in a computational model. Building on the compulsory informatics course (see previous table entry), it will focus on reactivity parameterisation and prediction. The basics of DFT calculations will be introduced, together with how DFT can be used to model reactions (including flaws, assumptions, drawbacks etc). Lecture based format will be complemented by practical sessions in setting up different DFT-based calculations.

This course will focus on recent progress in the application of kernel-based methods, Random Forests and Deep Neural Networks to modelling in chemistry. The material will build on the content of the core Informatics course and introduce new descriptors, advanced modelling techniques and example applications drawn from the current literature. Lectures will be interactive, with students working through computational exercises during class sessions.

An applied introduction to probabilistic modelling, machine learning and artificial intelligence-based approaches for students with little or no background in theory and modelling. The course will be taught through a series of case studies from the current literature in which modelling approaches have been applied to large datasets from chemistry and biochemistry. Data and code will be made available to students and discussed in class. Students will become familiar with python based tools that implement the models though practical sessions and group based assignments.

The course will introduce the general methodology of model development, including techniques for model identification and parameter estimation. The idea of model-based design of experiments will be introduced and linked to parameter estimation. Tools for model development and MBDoE will also be introduced.

The session will cover the use of electronic laboratory notebook which is a computer programme designed to replace laboratory notebooks. ELN will help the users to document research, experiments and procedures performed in a laboratory.

Process systems engineering (PSE) is a developed field of engineering, focusing on mathematical methods of optimisation of individual processes and systems of processes used in the manufacture of molecules. PSE tools include methods of identifying reaction kinetics, methods of model development, model-based design of experiments, analysis of system integration, and system optimisation tools. The application of PSE tools in petrochemical industry is well-developed and leads to major benefits in terms of process efficiency, safety and economics. The application of PSE tools in manufacture of more complex molecules and products, such as agrichemicals and pharmaceuticals, is less developed. This is mainly due to the difficulty in generating good models in the processes that are frequently not fully understood and not fully observed (not all species are monitored or identified). This course will cover key methods from PSE toolbox that are relevant for development of more complex synthetic chemistry-based manufacturing processes: methods of kinetics analysis, model-based design of experiments, use of models for process integration and optimisation. The course will be run as a workshop over two days.

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Date Availability
Wed 20 May 2020 09:00 [Places]
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