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March has been the month for the Spring School in Computational Chemistry for last 8 years. This time the school was overbooked already in November so if you want to join next year, register early.
Correspondingly, we decided to accept more participants than before resulting in tight seating and parallel sessions also for the last day hands-ons of the School. 31 researchers from Europe and beyond spent four science-packed days in occasionally sunny Finland.
The foundations of the school - the introductory lectures and hands-on exercises of (classical) molecular dynamics and electronic structure theory - have been consistently liked and found useful and have formed the core with small improvements.
For the last four years we've integrated the latest research paradigm, i.e. data driven science, also known as, machine learning (ML) to the mix. This approach has been welcomed by the participants, in particular as the lectures and hands-on exercises given by Dr. Filippo Federici Canova from Aalto University have been tailored for computational chemistry and cover multiple approaches to model data. ML is becoming increasingly relevant, as one of the participants, Mikael Jumppanen, noted in his flash talk quoting another presentation from last year: "Machine learning will not replace chemists, but chemists who don't understand machine learning will be replaced."
The ML day culminated in the sauna lecture given by prof. Patrick Rinke from Aalto University. He pitted humans against different artificial intelligence "personalities". The competition was fierce, but us humans prevailed with a small margin - partly because we were better at haggling for scoring.
This year we complemented the ML session with means to help create data to feed the algorithms. Accurate models require a lot of data, and managing hundreds or thousands of calculations quickly becomes tedious.
Marc Jäger from Aalto University introduced the relevant concepts, pros and cons of using workflows, spiced with the familiar hello world example. It was executed with FireWorks, a workflow manager popular in materials science. Once everyone had succeeded in helloing the world, Marc summarized that "this was probably the most difficult way of getting those words printed", but the actual point was, that if there is a workflow, or a complete workflow manager, which suits your needs, someone else has done a large part of the scripting work for you and you can focus on the benefits.
Workflow managers of course aren't a silver bullet beneficial in all research, but in case you need to run lots of jobs or linked procedures, automating and managing them with the right tool can increase productivity, document your work and reduce errors.
How do you make sense of the gigabytes of data produced by HPC simulations? It of course depends on what data you have. The School covered multiple tools to make sense of you data.
Visual inspection is a powerful tool in addition to averages, fluctuations and other numerical comparisons. MD trajectories or optimized conformations were viewed with VMD, electron density and structure were used to compute bonding descriptors using Multiwfn and NCIPLOT and a number of python scripts employing matplotlib for result visualization were given as real life examples on current tools.
Although computers keep getting faster, brute forcing research problems is not always the right way. In one of the parallel tracks on the last day, Dr. Luca Monticelli built on top of the MD lectures of the first day by presenting 6+1 enhanced sampling techniques to enable proper study of rare events.
The last one, coarse graining, strictly speaking is not an enhanced sampling method, but as it is orders of magnitude faster than atomistic simulations it can be used to equilibrate a system quickly enabling switching to atomistic detail from truly independent configurations.
The previous Spring Schools have included the possibility to present posters to facilitate the discussion among participants of one's own research with other participants and lecturers. Posters have helped to discover potential collaborations and new ideas to apply in one’s own research.
There is a lot of potential for collaboration as the School participants come from a highly diverse background as shown in the wordcloud below. The wordcloud is created from the descriptions filled in by the participants at the registration step.
Word Cloud: Scientific background of the participants.
One participant suggested in last year's feedback to replace the poster session with flash talks, which we now did. Each participant was asked to provide one slide to introduce the background, skills and scientific interests, and the slides were used in three minute flash talks to everyone else. The feedback was very positive, so we will likely continue with flash talks also in 2020.
Networking with researchers is yet another motivation to participate in the school. Philipp Müller from Tampere University of Technology took the initiative and proposed a LinkedIN group for the participants to keep in contact also after the school. This was realized on the spot and now the group has already most of the participants signed up.
As potential collaborations are discovered, the HPC-Europa3 programme, also presented in the School, can be used to fund 3-13 week long research visits. Or, if you choose your research visit to take place in Finland in March 2020, you could also participate to the School at the same time.
For the first time we asked the participants for their recommendation on who would benefit in participating in the school. The answers range from any under or post-grad student in the field to everyone who needs any computational skills. One participant also confessed that spending some time to learn elementary Python (as suggested) before the School would have been useful. The computational tools known to the participants at registration are collected to the picture below.
Word Cloud: Computational tools used by the participants.
The feedback also emphasized the quality of hands-ons, social events, and overall organization, while the pace of teaching sparked also criticism. This is understandable as the School covers a wide range of topics and therefore it is not possible to go very deep into details. Also, as the background of the participants is heterogeneous some topics are easier for some, but new to others. Partially this has been mitigated by organizing the hands-on sessions of the first two days in three parallel tracks with different difficulty.
The great majority of the participants was satisfied with all aspects of the school. Actually, our original aim has been to introduce the most important fundamental methods and some selected tools so that the participants are aware of them, and in case an opportunity to apply them comes, a deeper study will anyway be necessary.
Most of the lectures and hands-on materials are available on the School home page. The hands-on exercises in particular also also suitable for self study - take a look!