All Department of Chemistry courses

This course will cover the quantitative background to underpin many of the electrochemical methods exploited by the research groups in the Department.

• The first part ('Foundations') will cover the background physical chemistry relevant for electrochemical systems: Following a reminder of the basics of electrochemistry, we address the physical chemistry of metals, ionic solutions and the electrode/solution interface, including electrode dynamics, and connection to experimental measurements.

• The second part ('Applications') will cover specific examples of applications drawn from active research groups in the department.

Enrol onto the Moodle course and access notes here: https://www.vle.cam.ac.uk/course/view.php?id=254073

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.

Once you book this course, you will need to register for each session via Zoom.

This is a practical skills-based course which comprises of 1 optional drop-in session and 6 compulsory instructor-lead statistics sessions. These will take place in Todd Hamied

The Departmental Advanced Safety Training covers basic induction training in how to work safely, including emergency arrangements for fire and evacuation, first aid and incidents including flood and gas leak. By attending, you are made aware of the Department’s Health and Safety Policy and your responsibilities under health and safety law. You will be introduced to the process required to prepare a risk assessment with standard operating procedure (SOP) or method statement, how to select the correct type of protective equipment (PPE) and why it needs to be worn, and reminded of the importance of good house keeping for reducing the likelihood of there being an incident. The hazards associated with display screen equipment (DSE) and manual handling are identified and the need to control them by a suitable and sufficient assessment of the risk is explained. Electrical safety and the requirement for annual Portable Appliance Testing (PAT) is made clear.

• Please note you will find this training on Moodle.

Part of Induction Week

Advanced induction training for experimentalists introduces some of the department’s special chemical hazards including explosives, hydrogen fluoride and cyanide, and restricted chemicals, and illustrates the consequences of incorrect waste disposal. Experimentalists are made aware of the biological hazards in the department and how these are controlled with a suitable risk assessment, safety cabinets and the need for the appropriate inactivation method to be applied. Attendees are alerted to the hazards and damage caused by non-ionising radiation, glassware and sharps, oil baths and lifting equipment. The induction concludes by directing the experimentalist to compulsory University-provided specialist training courses, the requirement for fire awareness training and sources of Health and Safety information.

• This training is will be available on Moodle.

This course will cover safe storage and use of cryogens, safe use and stores of compressed gas, and aspects of oxygen depletion with respect to the above.

• This training will be available on Moodle.

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.

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.

There are 8 sessions in total DD1 to DD8 starting from 18th May and ending 10th June. The sessions are listed below:

DD1: Introduction to Drug Discovery and path to clinic Bobby Glen (UoC) 18th May, 10:00 - 12:00

SESSION CANCELLED DD2: Pharmacology + Biochemical and Biophysical methods Chris Stubbs (AZ) 20th May, 10:00 - 12:00

DD3: Structural Biology Gavin Collie (AZ) 25th May, 10:00 - 12:00

DD4: Hit generation methods and tactics Ben Whitehurst (AZ) 27th May, 10:00 - 12:00

DD5: Potency & thermodynamics Steve Atkinson (AZ) 1st June, 10:00 - 12:00

DD6: Computational methods (Session 1) - Modelling/MD/potency prediction/ML/AI Kathryn Giblin (AZ) 3th June, 10:00 - 12:00

DD7: Computational methods (Session 2) - Modelling/MD/potency prediction/ML/AI Bobby Glen (UoC) 8th June, 10:00 - 12:00

DD8: Impact of structures and physchem on DMPK/safety Jen Nelson (AZ) 10th June, 10:00 - 12:00

PhD research journey can present many challenges in our ability to maintain work life balance and work sustainably while progressing towards our goals. Addressing the challenges of work-life balance and sustainability in the context of a PhD is crucial for the well-being and effectiveness of researchers. This interactive session aims to create a space for researchers to hack key issues for developing sustainable academic practices and offers a toolkit of reliable, evidence-based strategies for wellbeing management

While leading others is often part of advancing our career – whether inside or outside of academia – often those in leadership positions do not receive training in how to lead and so do it badly. This full-day, practical and pragmatic course introduces participants to four essential ‘elements’ of leadership. In the process of doing so, it explores what leadership is and offers practical tools, strategies and examples to help you begin to lead others more effectively.

Join Cambridge Careers Consultant, Raj Sidhu for a discursive and interactive session where you will learn:

• What career options are open to you after a Chemistry PhD or PostDoc
• What alumni of the Department of Chemistry are doing now
• How to structure and approach career-thinking, during your PhD or PostDoc

All questions will be warmly welcomed throughout.

Trainer: Oleksandra Korychenska from Cambridge enterprise

What is Intellectual Property (IP)? Why does it matter to you? Who owns it? Who benefits? What is consultancy? What is a spin out? Why would you want to commercialise results from your research? What is it anyway? All this, and more, will be covered in a one-hour presentation by Cambridge Enterprise on the 12th of March. It is aimed at postgraduate students in Chemistry, after feedback showed that they would like to learn more about research commercialisation and IP. However, anybody is welcome to attend!

Training for PhD students:

Public engagement is increasingly seen as an important part of any research career, with the potential to give you the skills and insight to improve your research, make it more relevant and have impact. Rather than trying to engage with everyone, we’ll help you explore why you want to engage and who it would be valuable for you to have conversations with, and how, and where. We’ll introduce a logic model way of planning public engagement and sign post you to further training, support, advice and platforms for engagement across the University.

PhDs and Postdocs welcome, no prior knowledge required

Machine learning has become a common feature of many scientific papers, including chemistry, biology, and chemical biology. But what does it all mean? In this course, we will investigate the core features of common machine learning techniques such as principal component analysis (PCA), support vector machines (SVMs), and Random Forests, and how these can be applied to a real-world chemistry dataset. This course is meant to serve as a gentle introduction to machine learning - no prior knowledge is required.

Emma began her career as an experimental chemist, gaining her PhD in chemoenzymatic total synthesis under the tutelage of Prof. Hans Renata (The Scripps Research Institute, FL). During the course of her doctorate, she gained an interest in machine learning and how it can be applied to help chemists understand their systems. She thus joined the group of Dr. Alpha Lee (University of Cambridge) and later the group of Prof. Matthew Gaunt (University of Cambridge) to hone her expertise in computer programming, algorithm development, and machine learning for chemistry application.

'Enhance focus, reduce stress, use time more wisely and be more productive.

Learn to:

• Establish a method that works for you to enhance focus for the most important work (Deep Work)
• Reduce distraction and prioritise more effectively
• Establish 1 daily high quality mini break, to relieve stress, reduce self criticism and strengthen resilience
• Create the space to recognise your achievements each day - increase self awareness and confidence
• Combining proven neuroscience & mindfulness based techniques into useful daily habits.

In these sessions, Dr. Mukund S. Chorghade will discuss the pivotal role played by Process Chemistry / Route Selection in the progress of a chemical entity from conception to commercialization.

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These lectures will provide an introduction to the field with relevant background and theory, a survey of main strategies that have been used and are most widely practised and finally will cover current challenges and latest approaches in the area.

In this course we will give a brief introduction to the theory and simulation of molecules and materials. The focus will be on explaining at an introductory level the types of problems and properties that can be tackled with current techniques in theoretical chemistry. Limitations of current methods and future perspectives of where the field is heading and its intersection with modern experimental methods will also be discussed.

In these introductory lectures, you will learn how machine learning inspired methods have been making inroads into molecular modelling, particularly first principles modelling. The focus will be on descriptors and representations of atomic geometry and modelling potential energy surfaces.

The course will be a brief overview of polymer chemistry, covering a range of synthetic methods and interests in the context of drug delivery.

These lectures seek to provide an overarching vision of chemical synthesis methodology using machinery as enabling tools. They will highlight current capabilities and limitations in this highly digitally connected world and suggest where new opportunities may arise in the future, going well beyond our present levels of innovation and automation.