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Young Researcher's Workshop on Machine Learning for Materials Science 2019
Date: 06.05.2019 14:00 - 09.05.2019 13:00
Location: Aalto Design Factory + CSC training facilities
Location details: The workshop takes place at the Otaniemi Campus of Aalto University, located in Espoo city, across the western bay of Helsinki.
Aalto Design Factory (ADF) (Betonimiehenkuja 5C, 02150 Espoo) is a multi-purpose space dedicated to exchange of ideas. All scheduled talks, discussion and coffee breaks will be held in the ‘The Stage' area. Take advantage of ADF's cafe, common spaces and numerous discrete discussion spaces to work, discuss or relax.
The hands-on part on Tuesday is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.
Language: english-language
lecturers: Please see the official website:

  • free-price-finnish-academics.
  • free-price-others.

Workshop scope

Generalizing materials descriptors – How can we interpret and develop novel materials descriptors?

Inverse learning problems and generative models – How can we take advantage of the latest algorithm developed in the field of computer science?

Latent space and reinforcement learning approaches – Which problems in material science can be tackle through an efficient use of exploration and exploitation steps ?

Quantifying uncertainty in ML predictions – How can uncertainty prediction be a fully integrated and beneficial step in the study of material properties?

Direct application of ML to MD simulations – What are the available platforms and the current challenges for machine learning potentials fully exploitable by the community?

Industry partners 

We will hear talks from industry leaders on how ML algorithms are employed in their research and product development. Participants will be advertised in this space soon.


The event consists of two parts

Workshop school: 6-8 May 2018

Workshop conference: 8-10 May 2019

Details can be found at the ML4MS2019 web page: