Theme: SynTech CDT

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.

Join Zoom Meeting https://us02web.zoom.us/j/7507381413 Meeting ID: 750 738 1413

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.

During this workshop students will learn the appropriate strategies on how to write a research manuscript in subject areas related to the SynTech CDT. Participants will learn about when they should think about working towards writing up a paper, factors to consider when identifying the right journal in specific scientific area and practical writing tips. Students will also learn about the submission process and how to assess the feedback from the reviewers.

This workshop will be held online.

You will receive a link to sign into the workshop a few days before the session starts.

During this workshop students will learn how to develop skills in presenting information for a grant proposal and to arrange different sections. Participants will also learn how to review and respond to the feedback of assessors and how to revise proposals for resubmission in the case of rejection. By the end of this workshop students will gain a full understanding of the criteria most funders use to determine whether grant proposals are funded.

This will be an online workshop.

You will be sent a link to sign in closer to the date.

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.