OCAMM

COST NanoSpace - AI in Astrochemistry Training School 2025
2025-08-05 08:00 - 2025-08-08 17:00 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Training School Materials
Motivation and overview
Artificial intelligence (AI) and machine learning (ML) have suddenly emerged as game-changing tools across diverse scientific disciplines. This includes astronomy -including observational astronomy, laboratory astrophysics and astrobiology- and chemistry -where machine learning interatomic potentials are already solidly established tools for atomistic modeling of materials and molecules. At the intersection between chemistry and astronomy, astrochemistry is posed to similarly benefit from the opportunities for accelerated scientific discovery enabled by AI. The COST NanoSpace AI in Astrochemistry Training School 2025 aims to furnish our community with the necessary background to get started to utilize these tools, by bringing together world-renowned experts to Aalto University in Southern Finland. The School will provide COST NanoSpace action participants and young researchers with specialized knowledge and address the urgent need for training on AI tools in astrochemistry.
Program
The School will provide a starting point for astrochemistry researchers interested in adopting AI tools for their research. We will cover the basics as well as showcase various examples of how AI has been successfully used to tackle research problems in chemistry and astrochemistry.

Tuesday 5 August
Wednesday 6 August
Thursday 7 August
Friday 8 August

Time
Topic
Trainer
Topic
Trainer
Topic
Trainer
Topic
Trainer

9:00
Registration
Gaussian Processes and Regressors
Mads-Peter V. Christiansen
ML for Radio Interferometry
Ryan Loomis

9:30
Interpretable ML Techniques for Astrochemistry
Johannes Heyl

10:00
NanoSpace and AI in Astrochemistry TS Overview
Aníbal García-Hernández &
Edward Goldwyn

10:30
Coffee break
Coffee break
Coffee break
Coffee break

11:00
Fundamentals of ML
Miguel Caro
ML for Interatomic Potentials
Rina Ibragimova
ML Binding Energies of Astrochemical Molecules
Johannes Heyl
Exercise Session: ML for Interatomic Potentials
Rina Ibragimova

11:30

12:00

12:30
Lunch
Lunch
Lunch
Lunch

13:00

13:30

14:00
Basics of Neural Networks
Xabier Pérez Couto
Exercise Session: Bayesian Optimization with
Gaussian Processes
Mads-Peter V.
Christiansen
Neural Networks for Astrochemistry
Lorenzo Branca
Intelligent Crystal and Molecular Structure Search Algorithms
Isabelle Braems

14:30

15:00
Exercise Session: Neural Networks I
Xabier Pérez Couto
Exercise Session: ML in Astrochemistry I
Lorenzo Branca

15:30
Tea break
Tea break
Exercise Session: ML in Astrochemistry II
Lorenzo Branca
Tea break

16:00
Exercise Session: Neural Networks II
Xabier Pérez Couto
Atomistic Modelling for Radiation
Damage in Space
Andrea Sand
Tea break
Exercise Session (continuation)
Mads-Peter V.
Christiansen

16:30
Poster pitches
Excursion to Suomenlinna
Conclussions & goodbye

17:00
ML for Chemical Intuition
Brett McGuire

17:30

18:00

18:30

19:00
Dinner in Suomenlinna (paid by participants; participation optional)

Important dates

8 April 2025 – First Announcement
8 April 2025 – Registration starts
30 May 2025 – Registration deadline
15 June 2025 – Announcement of the final (and backup) lists of registered participants as well as those eligible for COST financial support
5-8 August 2025 – NanoSpace AI in Astrochemistry Training School at Aalto University’s School of Chemical Engineering’s main building

Trainers and Organizing Committee
The School is organized by COST NanoSpace in collaboration with Aalto University:

D. Aníbal García Hernández, COST NanoSpace Action Chair (IAC, Spain)
Brett A. McGuire, COST NanoSpace member (MIT, USA)
Miguel A. Caro, local organizer & COST NanoSpace member (Aalto, Finland)
Rina Ibragimova, local organizer & COST NanoSpace member (Aalto, Finland)
Dora Javor, local organizer (Aalto, Finland)

For questions pertaining to the local matters of the School, please get in touch with Miguel Caro (miguel.caro@aalto.fi).
Trainers and topics
The program will include sessions on the following topics as well as series of practical exercises:

Fundamentals of ML – Miguel Caro, Aalto Univ., Finland
Basics of Neural Networks – Xabier Pérez Couto, A Coruña Univ., Spain
Gaussian Processes and Regressors – Mads-Peter V. Christiansen, Aarhus
Univ., Denmark
Intelligent Molecular Structure Search Algorithms – Isabelle Braems,
IMN-CNRS, France
ML for Chemical Intuition – Brett McGuire, MIT, USA
ML for Radio Interferometry – Ryan Loomis, NRAO, USA
ML Binding Energies of Astrochemically Molecules and/or Interpretable ML Techniques for Astrochemistry – Johannes Heyl, UCL, UK
Neural Networks for Astrochem – Lorenzo Branca, Heidelberg Univ.,
Germany
ML for Protoplanetary Disk Chemistry and/or ML for Star Formation
Chemistry – (to be confirmed)
ML for Interatomic Potentials – Rina Ibragimova, Aalto Univ., Finland
ML for Radiation Damage in Space – Andrea Sand, Aalto Univ., Finland

Trainees
This is a list of the participants (in addition to the organizers and trainers) who will take part in the School.

Name
Institution

Alexia Anguera González
Institute of Spaces Sciences, University of Barcelona

Ransel Barzaga Guzman
IUDEA, La Laguna University

Chiara Beldì
University of Edinburgh

Lise Boitard-Crépeau
IPAG, UGA

Maren Brauner
Instituto de Astrofísica de Canarias, IAC

Jakub Bulička
Faculty of Mathematics and Physics, Charles University

Tadeus Carl
Chalmers University of Technology

Lorenzo Maria Casazza
University of Naples Federico II

Ayşe Ceylin Çelebi
University of Naples Federico II

Louey Charradi
Higher National Engineering School of Tunis

Gaëtan Clément
Institut des sciences moléculaires d’Orsay, ISMO

Lorenzo Demaria
Open University

Pooja Devi
Charles University, FZU

José Jairo Díaz Luis
Observatorio Astronómico Nacional, OAN-IGN

Elavenil Ganesan
Institute of physical chemistry polish academy of sciences

Gabriella Di Genova
University of Perugia

Rossella Di Giovanni
Scuola Superiore Meridionale, Scuola Normale Superiore

Amélie Godard Palluet
Centro de Astrobiología, CAB

Roya Hamedani Golshan
University of Cologne

Youssef Guermassi
Faculty of sciences of Bizerte

Alžběta Horynová
Institute of Physics of the Czech Academy of Sciences

Lukas Hrubcik
University of Chemistry and Technology Prague

Teresa Huertas Roldán
Instituto de Astrofísica de Canarias, IAC

Munavvar Husain
University of Warsaw

Gabriel Jaimes Illanes
Centro de Astrobiología, CAB

Stanka Jerosimic
University of Belgrade, Faculty of Physical Chemistry

Deniz Kacan
Purdue University

Alexandros Kyriazis
Universitat Autònoma de Barcelona

Rosell Martín
University of Extremadura

Sergio Mato Domínguez
University of Valladolid

Andrés Megías
Centro de Astrobiología, CAB

Enrico Di Micco
University of Naples Federico II

Milan Milovanovic
Faculty of Physical Chemistry, University of Belgrade

Alene Seyoum Mitiku
Ethiopian Space Science and Geospatial Institute, SSGI

Veronika Mitrokhina
Tartu Observatory, University of Tartu

Danial Mohammadi
KU Leuven, Stockholm University

Tanish Nandre
University of Bonn

Devismita Panda
Laboratoire d’Astrophysique de Bordeaux, University of Bordeaux

Theodore Pellegrin
Max Planck Institute for Extraterrestrial Physics

Sergey Pyrlin
CICECO – Aveiro Institute for Materials, University of Aveiro

W. M. C. Sameera
Chalmers University of Technology

Bethmini Senevirathne
University of Gothenburg

Milan Sil
Institut de Planétologie et d’Astrophysique de Grenoble

Jelena Lubura Stošić
Faculty of Technology, University of Novi Sad

Maxime Tanious
IPAG, IRAM

Dimitar Trifonov
Institute of Electronics, Bulgarian Academy of Sciences

Meenu Upadhyay
University of Basel

Luis Velilla-Prieto
CSIC – Institute of Fundamental Physics

Vojtech Vozda
Institute of Physics, Czech Academy of Sciences

Oerd Xhemollari
University of Cologne

Jaime Yepes de Paz
Complutense University of Madrid

Venue, transportation & accommodation
Venue

The conference will take place in the main campus of Aalto University, in the Helsinki metropolitan area. The venue is the Aalto University’s School of Chemical Engineering’s main building, Kemistintie 1, Espoo, 02150 Finland.
Getting there & around
Aalto University is very well connected within the Helsinki metro area. The Otaniemi campus can be reached via metro (“Aalto University” stop) and the light rail (“Maari”, “Aalto-yliopisto” and “Otaranta” stops, from west to east). Please visit the website of HSL, the company operating the public transport system in the Helsinki metro area. This includes buses, the metro, trains, downtown trams, the light rail, and some municipal ferry services (e.g., to the Suomenlinna fortress island). Public transport in the Helsinki metro area is safe, clean and reliable. The easiest way to use the public transport system is to download the HSL app on your phone.
The Aalto University campus can be reached from Helsinki airport, e.g., by combining the train with the metro (changing at Helsinki’s Central Railway Station). Non-collective public transport options like taxis are also available, including popular apps like Uber; these are significantly pricier than collective transportations but might be more convenient depending on the situation (e.g., if you are in a hurry to reach the airport).
Accommodation
Aalto University is located on the Otaniemi campus. Otaniemi is a district of the city of Espoo, located within the Helsinki metropolitan area. There are various on-campus accommodation options in Otaniemi, for instance the Radisson Blu Espoo hotel. Since the Otanimi campus is connected to Helsinki by metro, and the commuting time is ~15 minutes from Helsinki city center, it is entirely possible to stay in Helsinki and commute every day to the School. This opens the door to a wider variety of accommodation options in terms of quality and price. Two options for on-campus accommodation are:

Radisson Blu Espoo (from ~100 EUR/night)
Heymo 1 by Sokos (from ~85 EUR/night)

For accommodation options in Helsinki, popular aggregators like Booking.com offer a “map-view” of the city and their user ratings are usually a good indicator of quality and value. If you opt for booking accommodation in Helsinki city center and commuting every day to the Aalto campus, we recommend that you reserve a hotel within walking distance of a metro station for convenience, e.g., stations “Ruoholahti”, “Kamppi”, “Central Railway Station”, etc.
Registration
The School will be in person with attendance limited to 50-55 trainees and with priority given to PhD students and young researchers, who are strongly encouraged to participate. There is no registration fee and the NanoSpace COST Action will provide financial support (i.e., reimbursement after the event, covering full or partial travel, accomodation, and subsistence costs)* for a significant number of participants (at least ~20-25), with high priority to those with a primary affiliation in an institution located in an Inclusiveness Target Country (ITC)/Near Neighbour Country (NNC) participating in the Action**.
The information requested in the registration form below will be used to select the final list of registered participants as well as those eligible for financial support, which will be notified in advance of the Training School (i.e., by mid-June 2025). The attendees are expected to arrange their own travel and accommodation ( see Venue and Accomodation above for more information ).
To apply to take part in the School, please go to the registration website (deadline 30 May 2025): https://link.webropolsurveys.com/EP/88F312F3CB3ED0FF
*The applicants eligible for financial support (i.e., reimbursement after the event) are requested to consult the COST Annotated Rules for information about COST rules and procedures for Training Schools. The financial support does not necessarily cover all expenses related to participating in the Training School and they are contributions to the overall travel, accommodation and meal expenses of the trainee.
**Please consult the specific ITC and NNC countries and any COST special measures taken regarding the participation of researchers/innovators from some countries in the Annex I of Level A Country and Organisations.
About & contact
The AI in Astrochemistry Training School 2025 is part of the official activities of the COST Action NanoSpace, chaired by D. Aníbal García Hernández (IAC, Spain) and supported by COST (European Cooperation in Science and Technology). The main aim and objective of the COST Action NanoSpace (“Carbon molecular nanostructures in space”; CA21126) is to advance the fundamental understanding of the physics and chemistry of cosmic carbon nanomaterials (nanocarbons; nC) and their relevance in non-terrestrial environments by promoting the interdisciplinary combination of state-of-the-art astronomical, laboratory, and theoretical studies, among others. The main Action scientific challenges are attacked via an interdisciplinary approach, combining the expertise from a wide range of disciplines like observational astronomy, laboratory astrophysics, astrobiology, theoretical chemistry, synthetic chemistry, molecular reaction dynamics, material science, spectroscopy, graph theory, and data science (AI, big data). Researchers and innovators from all these fields are thus welcome to participate in the Action as Working Group members, applying here. Read more about NanoSpace.
As part of the core mission of COST NanoSpace, many events and activitites are organized every year in the framework of the Action, including Training Schools that provide high-level education on research topics and methods relevant to the objectives of the project. Events are organized by COST NanoSpace in collaboration with a local organizer. The AI in Astrochemistry Training School 2025 is hosted by the Aalto University Department of Chemistry and Materials Science. For more information about COST NanoSpace and the School, please contact the Action Chair (agarcia@iac.es); for practical matters about the school and Aalto University, please contact Miguel Caro (miguel.caro@aalto.fi).
Sponsors
The AI in Astrochemistry Training School 2025 is made possible thanks to the support by COST (European Cooperation in Science & Technology). COST is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.
Local support by the Department of Chemistry and Materials Science of Aalto University is also gratefully acknowledged.

Today 08-08-2025 you use ruffle!

TurboGAP School 2026
2026-03-23 09:00 - 2026-03-24 17:00 | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Motivation and overview
The field of atomistic ML has exploded in recent years. These models allow us to tackle materials modeling problems that were completely out of reach just a few years ago. Although most of the developments in this field have gravitated around ML interatomic potentials (MLPs), other properties which are a function of atomic coordinates, like core-electron binding energies, local electronic density of states, and so on, have also benefited from these developments. The software ecosystem has flourished in parallel with the methodological advancements. Unlike for DFT, where a few codes dominate, the MLP scene is fragmented due to the relative ease of producing functional code. While this might seem to be problematic, in practice it has helped drive many methodological advancements because the barrier to implementation of new features and experimental ideas is comparatively low and many more groups can participate.
The TurboGAP code (turbogap.fi) is developed at Aalto University by the Caro group, with support from CSC and participation of other local groups for specific features. The development of the code was initially motivated by the need for speeding up calculations based on GAP MLPs, but has since branched out; beyond-state-of-the-art methodologies in van der Waals corrections [PRB 111, 054103 (2025)] or inverse structure prediction [JACS 146, 14645 (2024); arXiv:2508.17132] have been developed and implemented within the TurboGAP development efforts. The code has full MPI support and a development branch, to be merged soon, has full MPI+GPU support. TurboGAP is, to our knowledge, the only MLP engine whose development is currently led by a Finnish institution. The purpose of this School is to gather the local community from Finnish higher-education and research institutions interested in MLP modeling of molecules and materials and introduce the general MLP methodology as well as the TurboGAP code specifically. The hope is that this will serve to both train local junior scientists in these new techniques and expand the TurboGAP user base, potentially also leading to new collaborations in development and applications. In addition, the School is keen to accept international attendees whose presence might contribute to these goals.
Participating in the School
The number of participants is limited to 20. Interested candidates can apply until 21 December 2025 at midnight (Finnish time) by following the link to the application form. The application form is very short, we only collect contact and institutional information as well as a brief motivation statement to participate. A maximum of 20 applicants will be invited by 31 December 2025 to participate and will have until 18 January 2026 to register. The registration is free of cost thanks to the financial support of CECAM-FI. If more than 20 applications are received, the selection of participants will be done by considering the motivation statement and trying to balance the participation from institutions across Finland and some representation from international institutions.
Program
All the lectures take place in room D311 (Ke 5) of the main building of the School of Chemical Engineering, Aalto University, Kemistintie 1, 02150 Espoo. The classroom is located on floor 3, one flight of stairs up from the buiding’s main entrance. The coffee and lunch breaks take place in an adjacent lobby.
All the hands-on sessions will be run within a JupyterLab virtual environment, Noppe, provided by CSC, the Finnish supercomputing center. The TurboGAP School’s lecturers have set up all the hands-on tutorials and you do not have to do any preparations to run the examples: just bring your own laptop, a web browser is enough. Instructions for using Noppe will be given to you during Session 2 of the School.

Monday 23 March

Tuesday 24 March

9:00-10:30

Session 1: Introduction to MLIPs and GAP (Miguel Caro)

Session 5: Radiation damage simulations (Ali Hamedani)

10:30-11:00

Coffee break

Coffee break

11:00-12:30

Session 2: Overview of the TurboGAP code, virtual environment for the hands-on session and basic example simulation (Miguel Caro & Tigany Zarrouk)

Session 6: Van der Waals corrections and their integration with MLIP (Heikki Muhli)

12:30-13:30

Lunch (catered)

Lunch (catered)

13:30-15:00

Session 3: Database curation and model training (Rina Ibragimova)

Session 7: Experimental observable prediction and reverse structure optimization: Molecular augmented dynamics (Tigany Zarrouk)

15:00-15:30

Break (smoothies)

Break (smoothies)

15:30-17:00

Session 4: Analysis and visualization of simulation results (Patricia Hernández-León)

Session 8: Grand canonical Monte Carlo simulations for variable-particle-number simulation (Alexis Front)

17:00-20:00

Social gathering at Masis Brewery (with pizzas, including vegan and vegetarian options)

The lecturers will be the TurboGAP development team, members of the Caro group, and collaborators:

Prof. Miguel Caro (TurboGAP team PI)
Dr. Tigany Zarrouk (TurboGAP lead developer)
Dr. Rina Ibragimova
Dr. Heikki Muhli
Patricia Hernández-León
Dr. Alexis Front
Dr. Ali Hamedani

Participants
Trainees

Name
Institution
Email

Biswarup Biswas
University of Turku
biswarup.biswas@utu.fi

Xiaoya Chang
Aalto University
xiaoya.chang@aalto.fi

Farshad Esmaeilian
University of Eastern Finland
farshad.esmaeilian@uef.fi

Leonardo Andres Espinosa Leal
VTT – Technical Research Center of Finland

Artem Glova
Aalto University
artem.glova@aalto.fi

Aapo Haavisto
Aalto University
aapo.2.haavisto@aalto.fi

Adam Harmat
Aalto University
adam.harmat@aalto.fi

Hanan Ibrahim
University of Jyväskylä
hanan.h.ibrahim@jyu.fi

Rasmus Ikonen
University of Jyväskylä
rasmus.l.o.ikonen@jyu.fi

Aatif Imran
University of Turku
aatif.a.imran@utu.fi

Jyri Kimari
University of Helsinki

Konstantinos Konstantinou
University of Turku
konstantinos.konstantinou@utu.fi

Tiago Lopes
University of Helsinki
tiago.lopes@helsinki.fi

Sanni Määttänen
Aalto University
sanni.maattanen@aalto.fi

Mario Mäkinen
Aalto University
mario.makinen@aalto.fi

Gianmarco Midena
Aalto University
gianmarco.midena@aalto.fi

Aneta Niklas
University of Oxford
aneta.niklas@chem.ox.ac.uk

Fatemeh Safari
University of Turku
fatemeh.f.safari@utu.fi

Nasrullah Sarker
University of Eastern Finland
nasarker@uef.fi

Timo Weckman
University of Jyväskylä

Litong Wu
University of Oxford
litong.wu@jesus.ox.ac.uk

Yongchao Wu
Aalto University
yongchao.wu@aalto.fi

DAS group members

Name
Institution
Email

Reece Brooking
Aalto University
reece.brooking@aalto.fi

Yuto Iwasaki
Fujitsu Ltd.
ext-yuto.iwasaki@aalto.fi

Ondrej Krejci
Aalto University
ondrej.krejci@aalto.fi

Sampsa Mäenpää
Aalto University
sampsa.maenpaa@aalto.fi

Veera Mäkimartti
Aalto University
veera.makimartti@aalto.fi

Henrik Stenbrink
Aalto University
nilshenriksamuli.stenbrink@aalto.fi

Matias Vartiainen
Aalto University
matias.vartiainen@aalto.fi

Milona Weck
Aalto University
milona.weck@aalto.fi

Yucheng Zhang
Aalto University
yucheng.zhang@aalto.fi

Lecturers

Name
Institution
Email

Miguel Caro
Aalto University
miguel.caro@aalto.fi

Alexis Front
Aalto University
alexis.front@aalto.fi

Ali Hamedani
Aalto University
ali.hamedani@aalto.fi

Patricia Hernández-León
Aalto University
patricia.hernandezleon@aalto.fi

Rina Ibragimova
Aalto University
rina.ibragimova@aalto.fi

Heikki Muhli
Aalto University
heikki.muhli@aalto.fi

Tigany Zarrouk
Aalto University
tigany.zarrouk@aalto.fi

Important dates

21 December 2025 – Deadline for applications
By 31 December 2025 – Notification to selected participants
18 January 2026 – Deadline for registration of accepted participants
23-24 March 2026 – TurboGAP School 2026

Venue, transportation & accommodation
Venue

The conference will take place in the main campus of Aalto University, in the Helsinki metropolitan area. The venue is the Aalto University’s School of Chemical Engineering’s main building, Kemistintie 1, Espoo, 02150 Finland.
Getting there & around
Aalto University is very well connected within the Helsinki metro area. The Otaniemi campus can be reached via metro (“Aalto University” stop) and the light rail (“Maari”, “Aalto-yliopisto” and “Otaranta” stops, from west to east). Please visit the website of HSL, the company operating the public transport system in the Helsinki metro area. This includes buses, the metro, trains, downtown trams, the light rail, and some municipal ferry services (e.g., to the Suomenlinna fortress island). Public transport in the Helsinki metro area is safe, clean and reliable. The easiest way to use the public transport system is to download the HSL app on your phone.
The Aalto University campus can be reached from Helsinki airport, e.g., by combining the train with the metro (changing at Helsinki’s Central Railway Station). Non-collective public transport options like taxis are also available, including popular apps like Uber; these are significantly pricier than collective transportations but might be more convenient depending on the situation (e.g., if you are in a hurry to reach the airport).
Accommodation
Aalto University is located on the Otaniemi campus. Otaniemi is a district of the city of Espoo, located within the Helsinki metropolitan area. There are various on-campus accommodation options in Otaniemi, for instance the Radisson Blu Espoo hotel. Since the Otanimi campus is connected to Helsinki by metro, and the commuting time is ~15 minutes from Helsinki city center, it is entirely possible to stay in Helsinki and commute every day to the School. This opens the door to a wider variety of accommodation options in terms of quality and price. Two options for on-campus accommodation are:

Radisson Blu Espoo (from ~100 EUR/night)
Heymo 1 by Sokos (from ~85 EUR/night)

For accommodation options in Helsinki, popular aggregators like Booking.com offer a “map-view” of the city and their user ratings are usually a good indicator of quality and value. If you opt for booking accommodation in Helsinki city center and commuting every day to the Aalto campus, we recommend that you reserve a hotel within walking distance of a metro station for convenience, e.g., stations “Ruoholahti”, “Kamppi”, “Central Railway Station”, etc.
Funding
The TurboGAP School 2026 is possible thanks to the financial support from CECAM-FI.

Seminar by Volker Deringer
2026-03-25 10:15 - 11:00 | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Prof. Volker Deringer from the University of Oxford will deliver an invited seminar on Wednesday 25 March 2025 at 10:15-11:00 in hall A304 (Ke2) of the Aalto University School of Chemical Engineering main building, Kemistintie 1, Espoo.
Machine-learned interatomic potentials for materials chemistry
Volker Deringer
Department of Chemistry, University of Oxford
Machine-learned interatomic potentials (MLIPs) are now widely used in atomistic simulations, giving access to length and time scales that are otherwise inaccessible to first-principles methods. Their reliability in practice, however, depends crucially on the quality and coverage of the training data. In this talk, I will discuss data-efficient approaches to constructing MLIPs, including ML-accelerated first-principles molecular dynamics for generating reference configurations, and automated de novo exploration of relevant configurational spaces using the autoplex software. I will also highlight model-distillation strategies (specifically, a teacher–student approach) using accurate but computationally costly models to train fast, task-specific MLIPs for downstream applications. I will illustrate these ideas using examples of structurally complex materials: amorphous silicon, graphene oxide, and phase-change materials that are used in data storage and neuromorphic computing.

ChemBio Finland: Session Machine Learning in Chemistry
2026-04-16 10:00 - 15:35 | Messukeskus, Messuaukio 1, Helsinki, 00520, Finland
As part of the ChemBio Finland 2026 Exhibition, held at Messukeskus in Helsinki, OCAMM and Aalto University professors Kari Laasonen and Miguel Caro are chairing a session on Machine Learning in Chemistry. The sympossium will take place on the second day of the ChemBio Finland event, 16 April 2026 between 10:00 and 15:35 in room 5 (“Sali 5”). The official website of the event is this, where you can find more information about the venue and the other parallel sessions.
Registration
The registration is free of charge. To register as a visitor, go to the official website or go directly to the registration page.
Program
The official web site for the program is here. There, you can also check the other sessions taking place in parallel at the same venue during ChemBio 2026.
Chairs: Kari Laasonen and Miguel Caro
Chairperson 10-12 Kari Laasonen

10:00–10:05 Opening. Kari Laasonen, Aalto University
10:05–10:30 Autonomous analysis, manipulation and reactions in Scanning Probe Microscopy. Nian Wu, Aalto University
10:30–10:55 Decoding Surface Adsorption Chemistry with Active Learning. Milica Todorovic, University of Turku
10:55–11:20 LUMI-AI: A novel platform for AI4Science. Pekka Manninen, CSC Tieteen tietotekniikan keskus
11:20–11:55 ML-trained interaction potential for simulations of graphite nucleation from paramagnetic Fe-C melt. Jaakko Akola, NTNU, Trondheim
12:00–13:00 Lunch break

Chairperson 13-14:15 Miguel Caro

13:00–13:25 Advancing Biomolecular Simulations with ML: GROMACS Integration and QM/ML/MM Synergies. Dmitry Morozov, University of Jyväskylä
13:25–13:50 Machine Learning in soft matter systems. Mikko Karttunen, University of Eastern Finland
13:50–14:15 Machine Learning in drug discovery. Serhii Vakal, Orion Pharma
14:15–14:35 small break/coffee break

Chairperson 14:35–15:25 N.N.

14:35–15:00 Inferring the atomic-scale structure of complex materials from experimental observables with MAD. Tigany Zarrouk, Aalto University
15:00–15:25 ML search for new catalyst structures. Timo Weckman, University of Jyväskylä
15:25–15:35 Closing. Miguel Caro, Aalto University
