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Department of Chemistry

Department of Chemistry course timetable

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Wed 24 May 2023 – Thu 30 Nov 2023

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May 2023

Wed 24
ST12 Machine Learning Quantum Chemistry new (1 of 2) Finished 14:00 - 16:00 Todd-Hamied

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.

Thu 25
ST10: Asymmetric Catalysis new (3 of 3) Finished 11:00 - 12:00 Unilever Lecture Theatre

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.

ST12 Machine Learning Quantum Chemistry new (2 of 2) Finished 14:00 - 16:00 Todd-Hamied

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.

June 2023

Mon 5
Chemistry- PG Scientific Writing workshop (In Person, Face to Face) new Finished 16:00 - 17:00 Unilever Lecture Theatre

This course explores how to write a paper and improve your writing style in Chemistry papers. The course will be particularly useful for those working on their first year report, MPhil thesis or an article.

This event is in-person only.

Tue 6
ST13 Polymer Chemistry new (1 of 2) Finished 14:00 - 16:00 Todd-Hamied

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

Thu 8
ST14: Enabling Technologies for Synthesis new (1 of 3) Finished 14:00 - 15:00 Todd-Hamied

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.

Fri 9
ST13 Polymer Chemistry new (2 of 2) Finished 14:00 - 15:00 Todd-Hamied

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

Tue 13
ST14: Enabling Technologies for Synthesis new (2 of 3) Finished 14:00 - 15:00 Todd-Hamied

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.

Thu 15
ST14: Enabling Technologies for Synthesis new (3 of 3) Finished 14:00 - 15:00 Todd-Hamied

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.

Tue 20

In order to use machine learning methods on molecular data, it is necessary to express molecular structures in a form which can be used as the input. This workshop will outline ways in which this challenge has been addressed, including the InChI, SMILES, fingerprints and other ways of expressing molecules as text strings. The strengths and weaknesses of the various approaches makes them suitable for different applications. What will be most appropriate for the molecular problems you are tackling?

Mon 26
ST17: Machine Learning for Chemists new (1 of 3) Finished 14:00 - 16:30 Todd-Hamied

Course provider: Timur Madzhidov

Course description: This is an advanced workshop providing a hands-on opportunity to work on several case studies in teams during the workshop. Several applications of classical ML and deep learning approaches in chemistry will be reviewed. As part of the tasks assigned to groups, the fundamentals such as data acquisition, preparation and modelling will be included.

Tue 27
ST17: Machine Learning for Chemists new (2 of 3) Finished 14:00 - 16:30 Todd-Hamied

Course provider: Timur Madzhidov

Course description: This is an advanced workshop providing a hands-on opportunity to work on several case studies in teams during the workshop. Several applications of classical ML and deep learning approaches in chemistry will be reviewed. As part of the tasks assigned to groups, the fundamentals such as data acquisition, preparation and modelling will be included.

Wed 28
ST17: Machine Learning for Chemists new (3 of 3) Finished 14:00 - 16:30 Todd-Hamied

Course provider: Timur Madzhidov

Course description: This is an advanced workshop providing a hands-on opportunity to work on several case studies in teams during the workshop. Several applications of classical ML and deep learning approaches in chemistry will be reviewed. As part of the tasks assigned to groups, the fundamentals such as data acquisition, preparation and modelling will be included.

July 2023

Wed 5
ST18 - Design & Analysis of Experiments by ML new (1 of 2) Finished 13:00 - 17:00 Wolfson Lecture Theatre

This complimentary hands-on workshop is offered to PhD students and researchers at University of Cambridge who want to learn more about design of experiments (DOE) and data analysis. DOE skills are highly demanded by industry and still under-represented in many university curricula. Design of experiments is a practical and ubiquitous approach for exploring multifactor opportunity spaces, and JMP offers world-class capabilities for design and analysis in a form you can easily use without any programming. To properly uncover how inputs (factors) jointly affect the outputs (responses), DOE is the most efficient and effective way – and the only predictable way – of learning. Unlike the analysis of existing data, designed experiments can tell you about cause and effect, drive innovation and test opportunities by exploring new factor spaces. In addition to classical DOE designs, JMP also offers an innovative custom design capability that tailors your design to answer specific questions without wasting precious resources. Once the data has been collected, JMP streamlines the analysis and model building so you can easily see the pattern of response, identify active factors and optimize responses.

In this course you will learn to understand why to consider DOE analyze experiments with a single categorical factor using analysis of variance (ANOVA) analyze experiments with a single continuous factor using regression analysis understand the difference between classical and optimal designs design, analyze and interpret screening experiments incl. Definitive Screening Design design, analyze and interpret experiments in response surface methodology augment designs for sequential experimentation apply robust optimization evaluate and compare designs understand advanced features like blocking, split-plot experiments and covariates

The format of this course will be a mix of concept presentations, live demos and hands-on exercises. Most examples are inspired by chemistry and biotech, but can be easily transferred to other fields like materials science, agri-food science or engineering. Attendees should have access to JMP Pro (pre-installed). JMP Pro 17 is available for all attendees from University of Cambridge for both Windows and Mac. No prior knowledge required. All content and demos will be shared with the participants.

Mon 10
ST18 - Design & Analysis of Experiments by ML new (2 of 2) Finished 13:00 - 17:00 Wolfson Lecture Theatre

This complimentary hands-on workshop is offered to PhD students and researchers at University of Cambridge who want to learn more about design of experiments (DOE) and data analysis. DOE skills are highly demanded by industry and still under-represented in many university curricula. Design of experiments is a practical and ubiquitous approach for exploring multifactor opportunity spaces, and JMP offers world-class capabilities for design and analysis in a form you can easily use without any programming. To properly uncover how inputs (factors) jointly affect the outputs (responses), DOE is the most efficient and effective way – and the only predictable way – of learning. Unlike the analysis of existing data, designed experiments can tell you about cause and effect, drive innovation and test opportunities by exploring new factor spaces. In addition to classical DOE designs, JMP also offers an innovative custom design capability that tailors your design to answer specific questions without wasting precious resources. Once the data has been collected, JMP streamlines the analysis and model building so you can easily see the pattern of response, identify active factors and optimize responses.

In this course you will learn to understand why to consider DOE analyze experiments with a single categorical factor using analysis of variance (ANOVA) analyze experiments with a single continuous factor using regression analysis understand the difference between classical and optimal designs design, analyze and interpret screening experiments incl. Definitive Screening Design design, analyze and interpret experiments in response surface methodology augment designs for sequential experimentation apply robust optimization evaluate and compare designs understand advanced features like blocking, split-plot experiments and covariates

The format of this course will be a mix of concept presentations, live demos and hands-on exercises. Most examples are inspired by chemistry and biotech, but can be easily transferred to other fields like materials science, agri-food science or engineering. Attendees should have access to JMP Pro (pre-installed). JMP Pro 17 is available for all attendees from University of Cambridge for both Windows and Mac. No prior knowledge required. All content and demos will be shared with the participants.

Tue 11

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.

October 2023

Thu 5

This session is compulsory for all experimentalists to attend and will provide useful information regarding analytical facilities at this Department including NMR, Mass Spectrometry, X-ray Crystallography, Microanalysis and Electron Microscopy. Short descriptions will be given of all available instruments, as well as explain the procedures for preparing/submitting samples for the analysis will also be discussed.

November 2023

Wed 1

The first half of this session will cover an overview of Raytracing versus 3D Modelling, an introduction to the free Raytracing programme Povray, running Povray (command line options). Making and manipulating simple shapes, camera tricks (depth of field, angle of view) and using other software to generate Povray input (e.g. Jmol)

The second half of the session is an introduction to 3D modelling and animation using the open source programme Blender. This will cover the installation and customisation of the Blender interface for use with chemical models, how to import chemical structures from Jmol and the protein data base (PDB), the basics of 3D modelling, and an introduction to Key-frame animation.

No previous experience with either 3D modelling or animation is required.

Mon 6
Chemistry: FS13 LaTex (Live Online Course Using Zoom plus drop in sessions) (1 of 3) Finished 16:45 - 17:15 CHEM Online Zoom 1

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.

Once booked you will receive a link to Zoom.

Fri 10

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.

Once booked you will receive a link to Zoom.

Tue 14

This series of lectures will support you to improve the standard of your scientific writing. It will be delivered in two parts covering all you need to know about research journals including:

  • Session 1: 'How to read a paper'
  • Session 2: 'How to write scientific papers and your thesis'
Fri 17

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.

Once booked you will receive a link to Zoom.

Tue 21

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.

This training is available via Moodle.

Wed 22

The first half of this session will cover an overview of Raytracing versus 3D Modelling, an introduction to the free Raytracing programme Povray, running Povray (command line options). Making and manipulating simple shapes, camera tricks (depth of field, angle of view) and using other software to generate Povray input (e.g. Jmol)

The second half of the session is an introduction to 3D modelling and animation using the open source programme Blender. This will cover the installation and customisation of the Blender interface for use with chemical models, how to import chemical structures from Jmol and the protein data base (PDB), the basics of 3D modelling, and an introduction to Key-frame animation.

No previous experience with either 3D modelling or animation is required.

You will receive a Zoom link when you register for this course

Thu 30

We find ourselves at a pivotal point in history for the long-term sustainability of our society and biome. It would be so easy to have a negative view about the future i.e. climate change is slowly baking us all to death. Last year alone was pretty intense - 1/3 of pakistan was flooded last year and arctic storms ravaged the US. Our climate is becoming more extreme and unpredictable. In 2 years time, we'll be closer to 2050 than the year 2000. We have no time to lose.

But this talk isn't about doomerism or trying to induce anxiety in you. It's about demonstrating how you, as a university graduate, highly trained in some technical field, can exert maximum leverage in the fight against climate change through the career choices that you make over the next 10, 20 or 30 years. In this talk, Dr Chadwick will highlight the exciting, state-of-the-art work ongoing around the planet in areas such as Green Hydrogen, The future of food, Carbon Dioxide Removal, Fusion, Fission, and Renewables - technologies all key to our sustainable future.

All with the goal of simply providing you with some inspiration and helping you to imagine how your skill sets might one day lend themselves to our collective goal of a sustainable world.

Climate change is daunting - but it also represents a massive opportunity to make the world better.


Dr Nicholas Chadwick received his MChem in organometallic chemistry from the University of Nottingham in 2012 before successfully studying for a PhD in materials science at University College London in 2015. After graduating he worked on the development of a range of early stage hardware technologies such as advanced transistor technologies, low-cost pollutant sensors for under-represented groups across Southern Asia and Mexico, and carbon capture technologies. He became convinced that direct air carbon capture (DAC) was the one thing we needed at scale to reach our net zero targets of 2050 and didn't have. After going on a bit of a journey scoping out opportunities he decided to co-found Mission Zero Technologies to commercialize and scale Direct Air Capture.