Spring School on Computational Chemistry 2018
The 7th Spring School on Computational Chemistry will be organized at CSC, Finland, on 13–16th March 2018. The School is a Prace Advanced Training Center (PATC) event and free to attend for the participants.
The School covers the most important aspects of computational chemistry tools for researchers in many fields, like biosciences, materials sciences and physics in addition to the different flavors of chemistry.
- Basics of chemistry: the tools we use aim to understand the chemistry of the target systems
- Basic Linux skills: the hands-on tutorials will be done partially with graphical user interfaces (GUIs) and partially with command line interfaces employing a variety of text based input files
- Basics of the Python programming language: the machine learning hands-on tutorials utilize Python. The School home page gives additional details on the requirements and points to suitable material for self-study in advance
"The School is truly international and attracts people from all over the world. In 2017 it was attended by 24 participants from eight different countries. Frequent breaks, two social events and relaxed atmosphere allow networking with future colleagues."
Lectures and hands-on – crash course to important methods
The program starts with half day introductory lectures of molecular dynamics and electronic structure theory. It is followed by parallel sessions of extensive hands-on tutorials at three levels of difficulty to suit a wide range of prior knowledge or specific interests. These first two days will give an intensive crash course to these essential methodologies but they will also be useful to those with previous knowledge on the topics.
The third major topic will be machine learning in chemistry. The data driven methods have become increasingly important also in chemistry over the last years and the School aims to give the participants an intensive overview of the theory behind the approach with specific examples related to chemistry research. The theory is put into practice via hands-on tutorials using Jupyter Notebooks.
Additional hands-on session will showcase the spanking new NOMAD Analytics Toolkit service, which enables researchers to carry out sophisticated analyses using computational chemistry generated raw data from the largest computational materials data repository in the world.
The three main methodological topics will be complemented with shorter, more specialized lectures which introduce researchers to useful methods and software. These include mixed quantum-classical simulations, enhanced sampling techniques, visualizing results with VMD.
How to prepare?
We recommend taking a look at the 2017 School materials to get an impression of the content and level of prior knowledge to optimally benefit from the School. All materials, presentation slides and hands-on input files are available on PRACE website.
Registration to 2018 School
Registration is open until 11th February 2018, but we expect the School to be fully booked well before that. We aim to accept registrations based on the suitability of the applicants within two weeks of registration. 2018 School program and registration is available on PRACE website.
Comments from previous participants
Right after the 2017 School we asked two participants about their experience. Magdalena Scharf and Matthäus Drabek from the Philipps-University, Marburg, joined the School in order to obtain a general overview of computational methods. The School extended the group's horizon on both research tools and methods, commented Drabek.
– The new tools sparked ideas for answering cheminformatics questions in a new way. It was interesting to see others with very different research topics but similar problems, and how they plan to solve them.
– The potential participants should have a look at the previous materials to get an idea of the prerequirements and familiarize the basics of e.g. Python, in advance, instructs Scharf and continues:
– The participants should have some background in computational chemistry as otherwise the electronic structure theory will be really hard. The molecular dynamics introduction works better as a crash course also for those with less or no previous experience. The Machine Learning section was too intense as there was so much new content in such a short time, that it was difficult to keep up. On the other hand, even if it was not possible to follow everything, many useful things were exposed and can be looked at again with time if needed.
When asked again in autumn of the impact in practice, Scharf and Drabek said that the participation had helped adopting molecular dynamics simulations and electronic structure methods as a research tool and made it easier to learn more about them.
– We recommend the School for everybody who is interested in these topics, agree both Scharf and Matthäus.
For additional infomation, please contact:
Senior Applications Specialist, CSC