Latest update: 8 Dec 2018

Winter School in Theoretical Chemistry 2018

This is the 34th Winter School

Machine Learning in Theoretical Chemistry

Helsinki, December 10th–13th, 2018


[Picture stripe]

Lecturers

Jörg Behler, Georg-August-Universität Göttingen, Göttingen, Germany

Michele Ceriotti, EPFL, Lausanne, Switzerland

Volker Deringer, University of Cambridge, Cambridge, UK

Jason Goodpaster, University of Minnesota, Twin Cities, USA

Olexander Isayev, University of North Carolina, Chapel Hill, USA

Anatole von Lilienfeld, University of Basel, Basel, Switzerland

Paul Popelier, The University of Manchester, Manchester, UK

Milica Todorovic, Aalto University, Espoo, Finland

Contributing speakers

Volker Settels & Andrew Palmer, BASF SE, Germany

Michael Sluydts, University of Ghent, Belgium

Julia Westermayr, University of Vienna, Austria


Programme

Monday 10 December

9:00

Welcome

9:15

Behler 1

High-Dimensional Neural Networks: Concepts and Applications, Part 1

10:00

Behler 2

High-Dimensional Neural Networks: Concepts and Applications, Part 2

10:45

Break

11:00

Behler 3

High-Dimensional Neural Networks: Concepts and Applications, Part 3

11:45

Lunch

13:00

Sillanpää

HPC-Europa3: Travel and collaborate with EC funding

13:10

Sluydts

Accelerating materials screening with machine learning

13:30

Popelier 1

Next generation force field design: state of the art and challenges

14:15

Popelier 2

Background to FFLUX: Quantum Chemical Topology and Gaussian Processes

15:00

Break

15:15

Deringer 1

Machine-learning potentials for materials chemistry: fundamentals

16:00

Deringer 2

Machine-learning potentials for materials chemistry: applications to amorphous materials

16:45

Break

17:00

Poster Session

 Tuesday 11 December

9:15

Goodpaster 1

Kernel Ridge Regression, Gaussian Processes, and Neural Networks in Quantum Chemistry

10:00

Ceriotti 1

Atom-density based representations for machine learning

10:45

Break

11:00

Ceriotti 2

Not only potentials: learning vectors and tensors

11:45

Lunch

13:00

Westermayr

Machine learning for excited-state molecular dynamics

13:20

Settels & Palmer

High-throughput forecasting of molecular properties in solution

13:45

Isayev 1

Predicting properties of inorganic materials with machine learning

14:30

Isayev 2

Neural Networks learning Quantum Chemistry

15:15

Break

15:30

Popelier 3

Results and Future Work

16:15

Todorovic 1

Global atomistic structure search with Bayesian Optimization

 Wednesday 12 December

9:15

Lilienfeld 1

Quantum Machine Learning in Chemical Space: Part 1

10:00

Lilienfeld 2

Quantum Machine Learning in Chemical Space: Part 2

10:45

Break

11:00

Goodpaster 2

Learning Electron Correlation: Part 1

11:45

Goodpaster 3

Learning Electron Correlation: Part 2

12:30

Lunch and free afternoon

13:00

(Annual meeting of the Computational Chemistry Section of the Finnish Chemical Societies)

 Thursday 13 December

9:15

Todorovic 2

Predicting molecular orbital energies with Kernel Ridge Regression

10:00

Todorovic 3

Deep-learning molecular spectra with Neural Networks

10:45

Break

11:00

Deringer 3

Data-driven learning and prediction of inorganic crystal structures

11:45

Isayev 3

Deep Learning and Generative Models for Inverse Molecular Design

12:30

Closing and departure

 


Further Information

Place: The lectures will take place in Lecture Hall A110, Department of Chemistry, Kumpula Campus, University of Helsinki. Google Maps might be helpful.
Registration: Registration is closed. Registration closed at midnight local time, 30 November. Note: Some e-mail addresses have been entered incorrectly during the registration. If you have registered, but did not receive, say, the welcome e-mail sent December 4th, then there are some problems with your mail address.
Posters and abstracts: A poster session will be held on Monday evening. The posters are browsable throughout the duration of the School.
Abstracts: Dead-line for submitting abstracts has passed. Check that your poster abstract is correctly included in the Abstract book (preliminary).
Fees: The School is free of charge for academic staff and students
Accommodation: Participants should make their own accommodation arrangements.
Questions: General questions concerning the School can be addressed to Dage Sundholm or Mikael Johansson
Address: Department of Chemistry
P.O. Box 55 (A.I. Virtasen aukio 1)
FI-00014 University of Helsinki
Finland
Email: winterschool@chem.helsinki.fi
How to find the campus: See Chemicum on Google Maps
Practical notes: Some things to consider.
  • Arriving at the airport, you can take the Finnair bus (6.90 €, for customers of all airlines) or the new train connection between the airport and the central railway station with ringrail trains I and P (5.50 €).
  • The Journey Planner for public transport, and Google Maps might be helpful. Wikipedia knows about public transport in Helsinki as well.
  • A taxi from the airport to the Kumpula campus (A.I. Virtasen Aukio 1) or the city centre should be around 40–50 €. Note: As of July 2018, taxi prices are unregulated, so a ride can in principle cost anything. Best to ask the driver beforehand! This applies to all taxi rides, not only from/to the airport.
  • Look at the latest weather reports before travelling as temperatures might drop down to -15 degrees for the Winter School week. This year, the temperatures seem to be quite evenly zero °C.
Eat and drink: Lunch is available in the various canteens of the campus. For the evenings, we have collected a list of places suitable for slightly larger groups. The ones in italics are also good for just a drink or three.
Other things of interest: Helsinki might be cold, but is not dead in December. Check out the #myhelsinki pages.

Get more information about interesting conferences from the Upcoming conferences list at the Computational Chemistry List, CCL.