Department of Chemistry course timetable

The main aim of giving a presentation to the public or a science venue is to present information in a way that the audience will remember at a later time. There are several ways in which we can improve this type of impact with an audience. This interactive lecture explores some of those mechanisms.

The main aim of giving a presentation to the public or a science venue is to present information in a way that the audience will remember at a later time. There are several ways in which we can improve this type of impact with an audience. This interactive lecture explores some of those mechanisms.

This session will require 4-5 volunteers to provide a 10 min talk which the session will show how to improve. Presenters in the following week's Peer to Peer presentations will be given priority booking for this event.

This lecture will provide an overview of the Department’s electron microscopy facility. It will cover the theory of Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), including cryo-TEM and tomography, as well as analytical techniques Energy-dispersive X-ray spectroscopy (EDX) and Electron Energy Loss Spectroscopy (EELS). Examples of how these techniques can be used to characterise a range of samples including polymers, proteins and inorganic materials will be shown.

A thorough awareness of issues relating to research ethics and research integrity are essential to producing excellent research. This session will provide an introduction to the ethical responsibilities of researchers at the University, publication ethics and research integrity. It will be interactive, using case studies to better understand key ethical issues and challenges in all areas. There are three sessions running, you need attend only one.

Spectroscopic methods in biochemistry and biophysics are powerful tools to characterise the chemical properties of samples in chemistry and biology, including molecules, macromolecules, living organisms, polymers and materials. Within the wide class of biophysical methods, infrared spectroscopy (IR) is a sensitive analytical label-free tool able to identify the chemical composition and properties of a sample through its molecular vibrations, which produce a characteristic fingerprint spectrum. An infrared spectrum is commonly obtained by passing infrared radiation through a sample and determining what fraction of the incident radiation is absorbed at a particular energy. The energy at which any peak in an absorption spectrum appears corresponds to the frequency of a vibration of a part of a sample molecule. One of the great advantages of infrared spectroscopy is that virtually any sample in virtually any state may be studied, such as liquids, solutions, pastes, powders, films, fibres, gases and surfaces can all be examined. In this introductory course, the basic ideas and definitions associated with infrared spectroscopy will be described. First, the possible configurations of the spectrometers used to measure IR absorption will be discussed. Then, the vibrations of molecules, inorganic and organic chemical compounds, as well as large biomolecules will be introduced, as these are crucial to the interpretation of infrared spectra in every day experimental life.

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined sub-population of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

Drug discovery is a complex multidisciplinary process with chemistry as the core discipline. A small molecule New Chemical Entity (NCE) (80% of drugs marketed) has had its genesis in the mind of a chemist. A successful drug is not only biologically active (the easy bit), but is also therapeutically effective in the clinic – it has the correct pharmacokinetics, lack of toxicity, is stable and can be synthesised in bulk, selective and can be patented. Increasingly, it must act at a genetically defined sub-population of patients. Medicinal chemists therefore work at the centre of a web of disciplines – biology, pharmacology, molecular biology, toxicology, materials science, intellectual property and medicine. This fascinating interplay of disciplines is the intellectual space within which a chemist has to make the key compound that will become an effective medicine. It happens rarely, despite enormous investment in time, money and effort. What factors make a program successful? I would like to briefly outline the process, but importantly to offer some key with examples of success

This course is made up of 8 sessions which will be based around the topics below: unlike other courses in the Graduate Lecture Series, it is essential to attend all 8 sessions to benefit from this training. Places are limited so please be absolutely certain upon booking that you will commit to the entire course.

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.

This graduate-level course gives an overview of machine learning (ML) techniques that are useful for solving problems in Chemistry, and particularly for the computational understanding and predictions of materials and molecules at the atomic level.

In the first part of the course, after taking a quick refresher of the basic concepts in probabilities and statistics, students will learn about basic and advanced ML methods including supervised learning and unsupervised learning.

During the second part, the connection between chemistry and mathematical tools of ML will be made and the concepts on the construction of loss functions, representations, descriptors and kernels will be introduced.

For the last part, experts who are actively using research methods to solve research problems in chemistry and materials will be invited to give real-world examples on how ML methods have transformed the way they perform research.