Events organised by AScI

Here is a list of events organised and planned as part of our thematic programme.
Aalto University/Unto Rautio

AScI Thematic Programme Workshop 2014

Time: Wednesday 3rd December, 2014
Place: AScI Common room 3th floor, use the lift in the middle of the TUAS building

9:00 Introduction

9:15 Talk 1 - O. Anatole von Lilienfeld - "Machine Learning Methods for the Sampling of Chemical Space from First Principles"

10:00 Talk 2 - Luca Ghiringhelli - "Big Data of Materials Science - Critical Role of the Descriptor"

10:45 Coffee

11:30 Talk 3 - Filippo Federici - "Genetics in classical atomistic simulations"

12:15 Talk 4 - Erik Aurell - "Methods to learn models with very many parameters"

13:00 Lunch

14:00 Talk 5 - Pekka Marttinen - "Bayesian machine learning"

14:45 Talk 6 - Michael Gutmann - "Likelihood-free inference"

15:30 Coffee

16:15 Talk 7 - Patrick Rinke - "Opportunities for machine learning in electronic structure theory and excited states calculations"

17:00 Talk 8 - Torbjörn Björkmann - "Overview of databases and machine learning efforts in condensed matter physics"

17:45 Closing

There will be a series of annual thematic workshops on the topic of applying machine learning to solve physics problems.


AScI Thematic Programme Workshop 2016

Time: Wednesday 3rd February, 2016
Place: AScI Common room 3th floor, use the lift in the middle of the TUAS building

12:55 - 13:00 Greetings and Introduction, Lasse Laurson

13:00 - 13:45 Research opportunity for Machine Learning in applied physics, Prof. Patrick Rinke and Milica Todorovic (COMP, Aalto University)

13:45-14:30 Research opportunity for Machine Learning in applied physics, Dr Torbjörn Björkman (Åbo Akademi)

14:30-14:45 Coffee Break

14:45-15:30 Case study of machine learning for modelling friction process, Dr Filippo Federici Canova and Dr Martha A. Zaidan (Aalto Science Institute and COMP)

15:30-16:15 Machine learning methods: probabilistic models, Prof. Aki Vehtari (AaltoPML, Aalto University)

16:15-17:00 Machine learning methods: Computational Inference Research, Prof. Jukka Corander and Dr. Michael Gutmann (COIN, University of Helsinki)

17:00-17:30 Coffee and Open discussion

International Workshop on Machine Learning for Materials Science

Workshop brings together key scientific players who are likely to deepen the understanding of materials science, computational physics and chemistry as well as machine learning.

Time: Wednesday-Thursday, 8-9 March, 2017
Place: ''Opetustila 1'' at Innopoli 3, Vaisalantie 8, Espoo, Finland

Download the workshop report here (PDF, 163 KB).

Workshop schedule

DAY 1: Wednesday, 8 March 2017

09:00 - 09:15 Greetings and Introduction

Dr. Lasse Laurson (Coordinator of AScI thematic research program on Machine learning strategies for optimising frictional properties of materials, Aalto University, Espoo, Finland)

09:15 - 10:00 Accurate Machine Learning Predictions for Materials Properties, Dr. Matthias Rupp (Theory Department, Fritz-Haber-Institute of the Max-Planck-Society, Germany)

10:00-10:30 Analysing and Rationalising Molecular and Materials Databases Using Machine-Learning, Dr. Sandip De (COSMO, EPFL, Switzerland)

11:00-11:45 Learning Interactions from Microscopic Observables, Dr. Albert Bartok-Partay (Rutherford Appleton Laboratory, Science and Technology Facilities Council, United Kingdom)

11:45-12:15 Machine Learning for Structural Diversity in Amorphous Carbon, Dr. Volker Deringer (Engineering Laboratory, University of Cambridge, United Kingdom)

13:30-14:15 Machine Learning meets Quantum Chemistry

Prof. Klaus-Robert Müller (Institute of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Germany)

14:15-14:45 Efficient Bayesian Inference of Surface Adsorbate Structure

Dr. Milica Todorovic (COMP, Aalto University)

15:15-18:00 POSTER session


DAY 2: Thursday, 9 March 2017

09:00 - 09:45 Machine learning and dissimilarity analysis predict new strategies for bottom-up nanomaterials assembly

Dr. Daniel Packwood (Institute for Integrated Cell-Material Sciences (iCeMS), Kyoto University, and Japan Science and Technology Organization (PRESTO), Japan)

09:45-10:15 Minimum Energy Path Calculations with Gaussian Processes

Olli-Pekka Koistinen (Dept. of Computer Science, Aalto University

10:45-11:30 Materials Design on Three Fronts: Fundamental Theory, Automation, and Machine Learning

Dr. Rickard Armiento (Department of Physics, Chemistry and Biology, Linköping University, Sweden)

11:30-12:00 Dynamical Simulation of Infrared Spectra with Neural Network Potentials

Michael Gastegger (University of Vienna, Austria)



A booklet with all workshop abstracts is available online (only), here!


Dr. Filippo Federici Canova <[email protected]>
Dr. Martha Arbayani Zaidan <[email protected]>


Principal investigators:

  • Academy Fellow Dr. Lasse Laurson (coordinator)
  • Prof. Adam S. Foster
  • Dr. Filippo Federici Canova (AScI fellow)
  • Dr. Martha Arbayani Zaidan (AScI fellow)
  • Contact: [email protected]
  • Other key People:
  • Dr. Ville Haapasilta (COMP CoE, Aalto)
  • Prof. Tapio Ala-Nissilä (COMP CoE, Aalto)
  • Prof. Martti Puska (COMP CoE, Aalto)
  • Prof. Mikko Alava (COMP CoE, Aalto)
  • Prof. Stefano Zapperi (CNR, Milan, FiDiPro at COMP, Aalto)
  • Prof. Jukka Corander (COIN CoE, University of Helsinki)
  • Prof. Samuel Kaski (COIN CoE, Aalto)


Martha A. Zaidan, Ville Haapasilta, Rishi Relan, Heikki Junninen, Pasi P. Aalto, Filippo F. Canova, Lasse Laurson, and Adam S. Foster. Neural network classifier on time series features for predicting atmospheric particle formation days. In The 20th International Conference on Nucleation and Atmospheric Aerosols, 2017. (submitted).

Martha A. Zaidan, Ville Haapasilta, Rishi Relan, Heikki Junninen, Pasi P. Aalto, Filippo F. Canova, Lasse Laurson, and Adam S. Foster. Machine learning classifier for determining aerosol particle formation days. In Proceedings of the LI Annual Conference of the Finnish Physical Society, 2017. (submitted)

Martha A. Zaidan, Filippo F. Canova, Lasse Laurson, and Adam S. Foster. Mixture of Clustered Bayesian Neural Networks for modeling friction processes at the nanoscale. Journal of Chemical Theory and Computation, 13:3-8, 2017.

David Z. Gao, Filippo Federici Canova, Matthew B. Watkins, and Alexander L. Shluger, Efficient parametrization of complex molecule-surface force fields, J. Comput. Chem. early view (2015).

Wei Chen, Adam S. Foster, Mikko J. Alava, and Lasse Laurson, Stick-slip control in nanoscale boundary lubrication by surface wettability, Phys. Rev. Lett. 114, 095502 (2015).

Claudio Manzato, Adam S. Foster, Mikko J. Alava, and Lasse Laurson, Friction control with nematic lubricants via external fields, Phys. Rev. E 91, 021504 (2015).

Wei Chen, Sampo Kulju, Adam S. Foster, Mikko J. Alava, and Lasse Laurson, Boundary Lubrication with a liquid crystal monolayer, Phys. Rev. E 90, 012404 (2014)

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