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Department of Chemistry

Department of Chemistry course timetable

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Thu 14 Nov – Tue 21 Jan 2020

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November 2019

Fri 15
Chemistry: FS13 LaTex [Places] 09:00 - 13:00 G30

This hands-on course teaches the basics of Latex including syntax, lists, maths equations, basic chemical equations, tables, graphical figures and internal and external referencing. We also learn how to link documents to help manage large projects. The course manual is presented in the style of a thesis and since you also receive the source code you also receive a template for a thesis.

Mon 18
Chemistry: CT9 Atomic Force Microscopy (1 of 2) [Places] 10:00 - 12:00 Unilever Lecture Theatre

Since introduction in 1986 by Binnig, Quate and Gerber, atomic force microscopy (AFM) has emerged as one of the most powerful scanning probe microscopy technique. The possibility to acquire three-dimensional morphology maps of specimens on a surface in both air and in their native liquid environment with sub-nanometre resolution makes it a very versatile single molecule technique. A conventional AFM topography map provides valuable information on the morphology and structure of heterogeneous biological samples, while single molecule force spectroscopy can interrogate the biophysical and nanomechanical properties of the sample at the nanoscale. Furthermore, the combination of AFM with spectroscopic modes enable to enquire the optical properties of the sample with nanoscale resolution. In these introductory lectures, the general capabilities of AFM with respect to other scanning probe and electron microscopy techniques will be discussed. The general principles governing the functioning of AFM in contact and tapping mode will be given, as well as the principles enabling the study of nanomechanical properties of samples by force spectroscopy and nanomechanical imaging. Other modes such as scattering SNOM, AFM-IR and Raman will be generally discussed. The course will provide the necessary background to acquire a morphology map by AFM. The last session will consist of a hand-on session introducing the students to the use and functioning of an AFM instrument.

Chemistry: CT9 Atomic Force Microscopy (2 of 2) [Places] 12:00 - 13:00 UB7

Since introduction in 1986 by Binnig, Quate and Gerber, atomic force microscopy (AFM) has emerged as one of the most powerful scanning probe microscopy technique. The possibility to acquire three-dimensional morphology maps of specimens on a surface in both air and in their native liquid environment with sub-nanometre resolution makes it a very versatile single molecule technique. A conventional AFM topography map provides valuable information on the morphology and structure of heterogeneous biological samples, while single molecule force spectroscopy can interrogate the biophysical and nanomechanical properties of the sample at the nanoscale. Furthermore, the combination of AFM with spectroscopic modes enable to enquire the optical properties of the sample with nanoscale resolution. In these introductory lectures, the general capabilities of AFM with respect to other scanning probe and electron microscopy techniques will be discussed. The general principles governing the functioning of AFM in contact and tapping mode will be given, as well as the principles enabling the study of nanomechanical properties of samples by force spectroscopy and nanomechanical imaging. Other modes such as scattering SNOM, AFM-IR and Raman will be generally discussed. The course will provide the necessary background to acquire a morphology map by AFM. The last session will consist of a hand-on session introducing the students to the use and functioning of an AFM instrument.

Tue 19
Chemistry: CT7 X-Ray Crystallography (2 of 2) In progress 14:00 - 15:00 Unilever Lecture Theatre

These lectures will introduce the basics of crystallography and diffraction, assuming no prior knowledge. The aim is to provide an overview that will inspire and serve as a basis for researchers to use the Department’s single-crystal and/or powder X-ray diffraction facilities or to appreciate more effectively results obtained through the Department’s crystallographic services. The final lecture will be devoted to searching and visualising crystallographic data using the Cambridge Structural Database system.

Wed 20
Chemistry: CDT Computational Parametrization new Not bookable 13:00 - 17:00 Todd-Hamied

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.

Thu 21
Chemistry: IS10 Reproducible Research and Software: With a Python Focus new [Places] 14:30 - 15:00 Unilever Lecture Theatre

Research reproducibility can be hard to get right. The aim of this talk is to raise awareness on the common pitfalls so you can confidently share your work for posterity. We will cover the dos and don’ts of data processing, how to comment on a script, and how to share it. Python will be used as an example because a variety of tools exist for this language. The goal is for anyone reading your paper to be able to go from the raw data to your paper figures.

The talk will last 20 minutes and there will be time for questions/discussion afterwards.

This talk is brought to you by the Chemistry Data Champions https://www-library.ch.cam.ac.uk/chemistry-data-champions

Fri 22
Chemistry: CDT Introduction to Probablistic Modelling new Not bookable 13:00 - 17:00 Todd-Hamied

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.

Mon 25
Chemistry: CT10 Vibrational Spectroscopy new [Places] 10:00 - 12:00 Unilever Lecture Theatre

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.

Wed 27
Chemistry: IS3 Research Information Skills [Places] 10:00 - 12:00 Unilever Lecture Theatre

This compulsory course will equip you with the skills required to manage the research information you will need to gather throughout your graduate course, as well as the publications you will produce yourself. It will also help you enhance your online research profile and measure the impact of research.

Thu 28
Chemistry: IS4 Research Data Management [Standby] 11:00 - 13:00 Todd-Hamied

This compulsory session introduces Research Data Management (RDM) to Chemistry PhD students. It is highly interactive and utilises practical activities throughout.

Key topics covered are:

  • Research Data Management (RDM) - what it is and what problems can occur with managing and sharing your data.
  • Data backup and file sharing - possible consequences of not backing up your data, strategies for backing up your data and sharing your data safely.
  • Data organisation - how to organise your files and folders, what is best practice.
  • Data sharing - obstacles to sharing your data, benefits and importance of sharing your data, the funder policy landscape, resources available in the University to help you share your data.
  • Data management planning - creating a roadmap for how not to get lost in your data!

Lunch and refreshments are included for this course

December 2019

Mon 2

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.

Tue 3
Chemistry: Green Chemistry new (1 of 2) [Places] 09:00 - 13:00 Unilever Lecture Theatre

This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;

  • Solvents - tools for analysing the merits and drawbacks of different solvents and tools for selecting the optimum solvent for chromatography, common reactions, work-ups and other purposes
  • Reagents - tools for analysing the merits and drwabacks of different reagents and substrate scope for some greener reagents for common transformations
  • Metrics: Yield, Atom Economy, Reaction Mass Efficiency, E-factor, Process Mass Intensity, Life Cycle Analysis and Carbon Footprinting
Chemistry: Green Chemistry new (2 of 2) [Places] 13:00 - 17:00 Unilever Lecture Theatre

This course will provide an overview of Sustainable Chemistry in the Pharmaceutical Industry: Motivation and Legislation It will cover the following in more detail;

  • Solvents - tools for analysing the merits and drawbacks of different solvents and tools for selecting the optimum solvent for chromatography, common reactions, work-ups and other purposes
  • Reagents - tools for analysing the merits and drwabacks of different reagents and substrate scope for some greener reagents for common transformations
  • Metrics: Yield, Atom Economy, Reaction Mass Efficiency, E-factor, Process Mass Intensity, Life Cycle Analysis and Carbon Footprinting
Wed 4

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.

Thu 5
Chemistry: CT8 Electron Microscopy [Places] 14:00 - 15:30 Unilever Lecture Theatre

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.

Fri 6
Chemistry: FS3 Integrity and Ethics in Research [Places] 14:00 - 16:00 Unilever Lecture Theatre

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.

January 2020

Mon 13
Chemistry: SC1-10 Statistics for Chemists (1 of 8) [Places] 13:00 - 15:00 G30

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.

Tue 14
Chemistry: IS4 Research Data Management [Places] 10:00 - 12:00 Todd-Hamied

This compulsory session introduces Research Data Management (RDM) to Chemistry PhD students. It is highly interactive and utilises practical activities throughout.

Key topics covered are:

  • Research Data Management (RDM) - what it is and what problems can occur with managing and sharing your data.
  • Data backup and file sharing - possible consequences of not backing up your data, strategies for backing up your data and sharing your data safely.
  • Data organisation - how to organise your files and folders, what is best practice.
  • Data sharing - obstacles to sharing your data, benefits and importance of sharing your data, the funder policy landscape, resources available in the University to help you share your data.
  • Data management planning - creating a roadmap for how not to get lost in your data!

Lunch and refreshments are included for this course

Chemistry: Machine Learning in Chemistry 101 new (1 of 7) [Full] 13:00 - 14:00 Chemistry of Health

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.

Wed 15
Chemistry: SC1-10 Statistics for Chemists (2 of 8) [Places] 10:00 - 12:00 G30

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.

Chemistry: DD1 The Drug Discovery Process [Places] 14:00 - 15:00 U203

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

Thu 16
Chemistry: Machine Learning in Chemistry 101 new (2 of 7) [Full] 13:00 - 14:00 Chemistry of Health

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.

Fri 17
Chemistry: DD2 The Drug Discovery Process [Places] 14:00 - 15:00 Todd-Hamied

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

Mon 20
Chemistry: SC1-10 Statistics for Chemists (3 of 8) [Places] 10:00 - 12:00 G30

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.

Tue 21
Chemistry: Machine Learning in Chemistry 101 new (3 of 7) [Full] 13:00 - 14:00 Chemistry of Health

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.