OCAMM

AI in CHEM Seminar Series: Intro session with Miguel Caro
2024-11-27 13:15 - 14:00 (Europe/Helsinki) | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
This talk is part of the “AI and Machine Learning in Chemical Research and Industry” Seminar Series organized by the Aalto University School of Chemical Engineering. It is open to all members of the public. Registered students in course CHEM-E4190 can also obtain 1cr by attending the seminars and completing the assignments.
Date and location

Wednesday 27 November 2024 @ 13:15-14:00
A304 Ke2 lecture hall in the main building of the School of Chemical Engineering, Kemistintie 1, 02150 Espoo.

Agenda

13:00-13:15. Setup and brief info for the registered students.
13:15-14:00. Seminar by Miguel Caro, lecture hall A304.
14:00-onwards. Coffee, netwoking and mingling in the lobby adjacent to the lecture hall.

Seminar info
Achieving a new degree of realism in materials modeling with machine learning
We are in the middle of an AI revolution in all aspects of society. However, machine learning had already been trending in chemistry and materials science for a few years before ChatGPT popularized the use of AI among the general public. After a brief introduction to the field of AI in chemical research, and links to the upcoming talks in the Seminar Series, I will give some examples, from our own group’s research activities, showcasing the use of atomistic machine learning to achieve a degree of realism in materials modeling that was previously out of reach.
About the speaker
Miguel Caro is Senior Scientist at the Department of Chemistry and Materials Science, Aalto University, as well as main organizer of the AI in CHEM Seminar Series and teacher in charge of course CHEM-E4190. For more info, visit miguelcaro.org.

Special seminar by Dr. Konstantinos Konstantinou
2024-11-01 13:15 - 14:00 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Save the date! OCAMM presents a special seminar by Dr. Konstantinos Konstantinou from the University of Turku, who will talk about atomistic simulations of phase-change materials for aerospace applications. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 1 November 2024 @ 13:15 in lecture hall D311 (Ke5) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to find out how order/disorder transitions in a material can be used to construct nanoscale computer memories!
Title
Non-volatile phase-change memory for spaceship application
Abstract
Radiation-hard non-volatile memories are in high demand by the space community for implementation in microcontrollers and solid-state data recorders. In phase-change memories, binary data are represented as changes in structural phase rather than by stored electrical charge; thus, these devices are supposed to be tolerant to ionizing radiation effects. Ion irradiation corresponds to a process that involves the production of non-equilibrium cascades in the host material, and the atomistic modelling of such events in glasses is challenging. Here, radiation damage in amorphous Ge2Sb2Te5 phase-change memory material is modelled by carrying out thermal-spike simulations with ab initio molecular-dynamics calculations. A stochastic boundary-conditions approach is employed to treat the thermal nature of the cascades and drive the modelled system back to equilibrium in a natural way. The dynamics of the cascade evolution shows that the time-scale of the ballistic phase of the cascade inside the glass model is very short. Investigation of the atomic geometry highlights a structural recovery from the damage imposed during ion irradiation, since the glass manages to maintain its amorphous network. Analysis of the bonding for all the species in the glass structure reveals particular structural modifications in the local atomic environments and the connectivity of the amorphous network. Overall, the simulations manifest the remarkable ability of Ge2Sb2Te5 phase-change memory material to be radiation-tolerant, hence indicating its potential applications in future space and other radiation-present environments.
About the speaker
After completing his MSc degree in Computational Physics at the Aristotle University of Thessaloniki in Greece, Konstantinous moved to the UK to obtain a PhD in Physics from University College London. He then joined the University of Cambridge as a research associate in Chemistry. Konstantinos came to Finland in 2020 as postdoctoral researcher in Tampere University before joining the University of Turku, where he currently holds the prestigious Academy Fellow position. His current research interests include defects in amorphous semiconductors, resistive switching memories, machine-learned molecular-dynamics simulations, charge trapping processes, and electronic excitations, among others.

Special seminar by Dr. Michael Nolan
2024-08-08 08:00 - 17:00 UTC
Save the date! OCAMM presents a special seminar by Dr. Michael Nolan from the Tyndall National Institute, Ireland on ab initio atomistic simulation of the chemical processes involved in atomic layer processing, such as atomic layer deposition (ALD) and atomic layer etching (ALE). This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 9 August 2024 @ 9:30 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn more about how you can simulate the growth (and destruction!) of materials one atomic layer at a time!
Title
First Principles Simulations of Atomic Level Processing
Abstract
Atomic level processing allowed for deposition and etch of materials on complex substrates giving a high level of control over conformality and uniformity. In this seminar I will discuss how first principles simulations provide insights into a range of process chemistries. This includes atomic layer etching of high-k oxides, deposition of hybrid organic-inorganic materials and plasma deposition of metals.
About the speaker
Dr. Michael Nolan is head of the Materials Modelling for Devices group at the Tyndall National Institute in Cork, Ireland, whose aim is “to understand and predict how devices work, by research based on modelling materials at the atomic scale”. The applied focus areas are, among others, electronics, solar energy conversion, atomic-layer processing of materials, and surface-based heterogeneous catalysis. Dr. Nolan carries out his modeling work in close collaboration with experimental groups and industry.

Recent Advances in Computer-aided X-ray Spectroscopy
2024-06-17 14:00 - 2024-06-20 17:00 (Europe/Moscow) | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Recent Advances in Computer-aided X-ray Spectroscopy

Motivation and overview
X-ray spectroscopic techniques, such as X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS), as well as different related techniques, have been instrumental in improving our understanding of the structure of complex materials. However, direct interpretation of experimental X-ray spectra is often hindered by overlapping signatures, e.g., two different atomic environments can contribute in the same region of a material’s spectrum, making it impossible to tell them apart. At the same time, experimentalists often rely on simplified theoretical references to interpret their spectra. For these reasons, simulations have always had an important role in interpretation of X-ray spectra since electronic-structure-based computations of core-level excitation energies became practical a couple of decades ago. Still, many challenges remain regarding the accuracy and cost of these computations, and how to reliably combine them with experiment. The field has seen rapid growth and development in recent years, due to fundamental and methodological development (e.g., the development of low-scaling algorithms for core-level spectroscopy) as well as the introduction of machine-learning techniques which are opening up avenues that were not previously obvious, both in terms of speeding up the computational predictions and integrating simulations and experiments.
About this workshop
This workshop aims to get the community of computational X-ray spectroscopy together, and discuss recent advances, outstanding challenges, and the directions in which the field will be developing in the near and medium future. In particular, the workshop will cover advances in 1) highly accurate electronic structure techniques, 2) methodologies for high-throughput spectra generation and materials screening, 3) machine learning to speed up X-ray spectra predictions, 4) machine learning to aid in the interpretation of experimental spectra, 5) structure inference from spectra, 6) novel techniques combining experimental and simulated spectra, 7) applications to specific materials and molecules.
While the event is focused on computational approaches, one of the aspects we want to highlight are the developments combining experiment and simulation, and we want to hear from experimentalists what are the outstanding challenges and how they understand the role of computations in aiding in the interpretation of experimental results. For this reason we have included experimental experts in the roster of proposed speakers below.
Invited Speakers

Annika Bande (Hannover University & HZ Berlin, Germany)
Maria Chan (Argonne National Lab, US)
Dorothea Golze (TU Dresden, Germany)
Deyu Lu (Brookhaven National Lab, US)
Reinhard Maurer (University of Warwick, UK)
Mounir Mensi (EPFL, Switzerland)
Robert Palgrave (UCL, UK)
Paavo Penttilä (Aalto University, Finland)
Anna Regoutz (UCL, UK)
Sami Sainio (Aalto/SLAC)

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.
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).
Important information about disruptions to the Metro service during Summer 2024
There are ongoing renovation works during the Summer months of 2024 that will impact Metro services. The most important disruptions are:

Closing of the Helsinki Central Railway Station Metro station during 3 June – 1 September 2024. While the Central Railway Station will keep operating regular train services (both short and long distance), travelers will not be able to transfer to the Metro line at this station. The westbound Metro services will operate from Kamppi station, which will be the East terminus of the Metro line during this time. Travelers wishing to go to Aalto University with the Metro should thus board at Kamppi Metro station or any other station west of Kamppi (e.g., Ruoholahti). Do not board the Metro line at any station east of the Central Railway Station, as those Metro trains are only eastbound and do not go to Aalto University. If you are arriving in the Central Railway Station by regular rail service, e.g., with the train from the ariport, you can either walk to Kamppi (5-10 minute walk) or use tram lines 9 or 9B – it is probably easiest to walk and enjoy Helsinki city center. More info here.
Tietotie exit of the Aalto University Metro station is closed starting 3 June for about 5 weeks. The Aalto University Metro station has two exits/entrances: Tietotie and Otaniementie. The exit closest to the conference venue, Otaniementie, will remain open during the conference. Thus, this disruption should have very little impact on RACXS participants. Read more here.
Mellunmäki Metro station closed during 3 June – 8 September. This only affects public transit in the East Helsinki region and should not affect the vast majority of RACXS participants. If you want to read more about this, click here.

Schedule and program
Some of the speakers agreed to share their talks publicly.
We will start in the afternoon (~2pm) of Monday 17 June (so that participants from Europe can travel in the morning) and end with a full day on Thursday 20 June. This is the detailed program of the workshop.

Monday 17 June

Tuesday 18 June
Wednesday 19 June
Thursday 20 June

9:30
Mounir Mensi (invited):
“Dealing with the Surjective Nature of XPS”
Reinhard Maurer (invited):
“X-ray spectroscopic signatures of chemical bonding and dynamics at metal-organic interfaces”
Maria Chan (invited):
“Data-driven capture and analysis of core-level spectra”

10:10
Javier Heras-Domingo (contributed):
“Unlocking the Potential of EXAFS: Machine Learning Approaches for Spectroscopic Data”
Dylan Morgan (contributed):
“Using Orbital-Constrained DFT to Simulate Surface Spectroscopy with Relativistic Corrections”
Aneta Gójska (contributed):
“The K-X-ray intensity ratios as a tool of examination and thickness measurements of coating layers”

10:30
Coffee break

11:00
Sami Sainio (invited):
“Interpretation of experimental spectra and some related pitfalls”
Deyu Lu (invited):
“Data-driven X-ray absorption spectral analysis: theory, workflow, database and machine learning”
Anna Regoutz (invited):
“Recent developments in Hard X-ray Photoelectron Spectroscopy and Combination with Theoretical Approaches”

11:40
Sam Hall (contributed):
“X-ray Absorption Spectra Prediction of Extended Graphene Oxide Nanoflakes using Graph Neural Networks”
Bo Zhao (contributed):
“Automated Mössbauer spectroscopy”
Michael Walter (contributed):
“Predicting x-ray absorption and photoemission spectra on the absolute energy scale”

12:00
Lunch at Maukas
Lunch at Maukas
Lunch at conference venue

13:30
Robert Palgrave (invited):
“Quantitative Simulation of XPS Valence Band Spectra from DFT”
Annika Bande (invited):
“Prediction AND INTERPRETATION of XAS Spectra using Graph Neural Networks”
Conor Rankine (contributed):
“Teaching Core-Hole Spectroscopy to a Deep Neural Network”

14:00
Welcome to participants at Kemistintie 1 (coffee served)
13:50
Thomas Pope (contributed):
“Refining the Syllabus: Can Physically Motivated Descriptors Improve Predictions”

14:10
Lena Bäuml (contributed):
“Following the Coupled Nuclear and Electron Dynamics of Chlorophyll with XMS-CASPT2 X-Ray Absorption Spectra”
Navanthara Karippara Jayadev (contributed):
“Insights to the Auger decay in benzene”
Bibek Samal (contributed):
“A Parameter-Free Approach for Modeling X-ray Emission and Photoelectron Spectroscopy”

14:30
Dorothea Golze (invited):
“Accurate prediction of core-level spectra with GW for complex materials”
14:30
Coffee break

15:10
Paavo Penttilä (invited):
“Upgrading X-ray scattering analysis of wood through modeling and machine learning”
15:00
Xing Wang (tutorial):
“Streamlining Core-Level Spectroscopy Calculations with AiiDAlab Quantum ESPRESSO App”
Tigany Zarrouk (tutorial):
“TurboGAP : Machine-Learned Potential Atomistics with Experimental Observable Prediction/Optimization”

15:50 – 17:00
Mingling (snacks & refreshments)
16:00 – 17:30
Poster session (snacks & refreshments)

19:00 – 21:00

Dinner at Restaurant Fat Lizard Otaniemi

Participants

Abdurrahman
Adhyatma
University of Turku
abadhy@utu.fi

Annika
Bande
Hannover Univeristy and Helmholtz-Zentrum Berlin

Lena
Bäuml
Ludwig Maximilian University of Munich

Prajna
Bhatt
UCL

Miguel
Caro
Aalto University
miguel.caro@aalto.fi

Maria
Chan
Argonne National Laboratory
mchan@anl.gov

Prathibha
Chandrashekhar
Uppsala University
prathibha.chandrashekhar@physics.uu.se

Carlos
Corral-Casas
Imperial College London
c.corral-casas@imperial.ac.uk

Mandira
Das
University of Turku
mandda@utu.fi

Dhilan
Devadasan
Thermo Fisher Scientific
dhilan.devadasan@thermofisher.com

Andrea Filippo
Di Feo
LUT University
andrea.di.feo@lut.fi

Aneta
Gójska
National Centre for Nuclear Studies
aneta.gojska@ncbj.gov.pl

Dorothea
Golze
TU Dresden
dorothea.golze@tu-dresden.de

Sam
Hall
Helmholtz-Zentrum Berlin
samuel.hall@helmholtz-berlin.de

Javier
Heras Domingo
ICIQ
jheras@iciq.es

Rina
Ibragimova
Aalto University
rina.ibragimova@aalto.fi

Nayanthara
K. Jayadev
University of Southern California
karippar@usc.edu

Jiahui
Kang
Aalto Universtiy
jiahui.kang@aalto.fi

Jannis
Kockläuner
TU Dresden
jannis.kocklaeuner@mailbox.tu-dresden.de

Alisher
Kumarov
LUT
alisher.kumarov@lut.fi

Deyu
Lu
Brookhaven National Laboratory
dlu@bnl.gov

Reinhard
Maurer
University of Warwick

Mounir
Mensi
EPFL – Switzerland
mounir.mensi@epfl.ch

Clelia
Middleton
Newcastle University

Dylan
Morgan
University of Warwick
dylan.morgan@warwick.ac.uk

Holger
Neupert
CERN
Holger.Neupert@cern.ch

Robert
Palgrave
UCL
r.palgrave@ucl.ac.uk

Paavo
Penttilä
Aalto University

Thomas
Pope
Newcastle University
thomas.pope2@newcastle.ac.uk

Paulina
Prslja
Aalto university
paulina.prslja@aalto.fi

Ramesh
Raju
Aalto University
ramesh.raju@aalto.fi

Conor
Rankine
University of York

Anna
Regoutz
University College London

Sami
Sainio
Aalto/SLAC

Bibek
Samal
Tata Institute of Fundamental Research
bibeksamal8@gmail.com

Ari Paavo
Seitsonen
École Normale Supérieure
Ari.P.Seitsonen@iki.fi

Torsten
Staab
University Würzburg – LCTM
torsten.staab@uni-wuerzburg.de

Milica
Todorovic
University of Turku

Krystian
Trela
National Centre for Nuclear Studies
krystian.trela@ncbj.gov.pl

Ryan
Trevorah
University of Helsinki
ryan.trevorah@helsinki.fi

Xing
Wang
Paul Scherrer Institute
xing.wang@psi.ch

Yanzhou
Wang
Aaalto University
yanzhou.wang@aalto.fi

Michael
Walter
University of Freiburg
Michael.Walter@fmf.uni-freiburg.de

Yerkezhan
Yerkinbekova
LUT University
yerkezhan.yerkinbekova@lut.fi

Tigany
Zarrouk
Aalto University
tigany.zarrouk@aalto.fi

Bo
Zhao
TU Darmstadt
bo.zhao@tmm.tu-darmstadt.de

Registration
The registration closed on 5 April 2024. If you applied, you should have already been notified with a decision regarding your application.
Organizers
Scientific organizing committee

Miguel Caro, Aalto University (Finland)
Patrick Rinke, Aalto University (Finland)
Tigany Zarrouk, Aalto University (Finland)

Practical matters

Dora Javor, Aalto University (Finland)

Affiliations
This workshop is organized by OCAMM and affiliated to the Finnish Synchrotron Radiation User Organisation (FSRUO).

Sponsors
In addition to organization support by OCAMM, the Recent Advances in Computer-aided X-ray Spectroscopy conference is financially supported by the Psi-K network, the Finnish CECAM node, the Aalto University Deparment of Chemistry and Materials Science, and the Aalto Science Institute.

Seminar by Prof. Volker Deringer on machine-learning-based simulation of amorphous materials
2024-03-18 14:15 - 15:15 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
OCAMM presents a special seminar by Prof. Volker Deringer from the University of Oxford, UK on machine-learning-based simulation of amorphous materials. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 18 March 2024 @ 14:15 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to delve into the intricate atomic structure and fascinating properties of amorphous materials!
Title
Machine-learning-driven advances in modelling amorphous materials
Abstract
Understanding the connections between the atomic-scale structure of materials and their macroscopic properties is among the most important research challenges in solid-state and materials chemistry. Atomistic simulations based on quantum-mechanical methods have played a key role in this – but they are computationally demanding, and therefore they will inevitably reach their limits when materials with highly complex structures are to be studied. Machine learning (ML) based interatomic potentials are a rapidly emerging approach that helps to overcome this limitation: being “trained” on a suitably chosen set of quantummechanical data, ML potentials achieve comparable accuracy whilst giving access to much larger-scale simulations – with thousands or even millions of atoms.
In this presentation, I will showcase some recent advances in the modelling and understanding of inorganic materials that have been enabled by ML-driven simulations. I will argue that ML potentials are particularly useful for modelling non-crystalline (amorphous) structures that are difficult to characterise experimentally. I will survey recent work ranging from structural transitions in amorphous silicon [1] to multicomponent systems – specifically, chalcogenide phase-change materials used in digital data storage [2]. I will also discuss methodological aspects, including a perspective for using large synthetic datasets to pre-train neural-network potentials which can subsequently be fine-tuned on quantum-mechanical data [3]. The development of new, accurate and efficient atomistic ML models promises a way to more fully understand the structure and properties of amorphous materials on the atomic scale.
[1] V. L. Deringer, N. Bernstein, G. Csányi, C. Ben Mahmoud, M. Ceriotti, M. Wilson, D. A. Drabold, S. R. Elliott, Nature 2021, 589, 59.
[2] Y. Zhou, W. Zhang, E. Ma, V. L. Deringer, Nat. Electron. 2023, 6, 746.
[3] J. L. A. Gardner, K. T. Baker, V. L. Deringer, Mach. Learn.: Sci. Technol. 2024, 5, 015003.

LOBSTER School 2024
2024-03-12 - 2024-03-14 | Aalto Design Factory, Puumiehenkuja 5A, Espoo, 02150, Finland
LOBSTER School on Chemical Bonding Analysis

Motivation and overview
The need to understand why a particular material is stable or not is of paramount importance for computational materials science. Today, atomic-scale materials modelling is clearly dominated by high-performance density-functional theory (DFT) using plane waves and pseudopotentials, but understanding the often incredibly complex results frequently benefits from a thorough chemical-bonding analysis using local orbitals. This 3-day school will teach ~25 participants how to carry out chemical-bonding analysis in general. In particular, the computer program LOBSTER will be introduced, which has been designed to suit the needs of high-performance materials simulations by being able to process output from VASP, ABINIT and Quantum ESPRESSO. The school will be the fifth in a series of previous successful ones in Germany, China, Japan, and an online school during the COVID-19 pandemic. It is targeted at researchers from various fields of computational science such as chemistry, physics, and materials science.
Methods for chemical-bonding analysis
As the search for new materials based on increasingly CPU-affordable electronic structure calculations gathers pace, and the amount of data generated by high-throughput calculations continues to accumulate, it becomes important to develop effective quantitative techniques to make sense of this wealth of information. While electronic structure calculations, predominantly based on DFT, have proven increasingly reliable and useful in understanding and predicting the properties of solids, the intricate contribution of local bonding networks in these materials escapes the most commonly used DFT analysis tools. A standard tool to probe the local character of the material’s band structure is the projected density of states (PDOS). Unfortunately, the PDOS only provides information about where electrons reside in the material, and not about how much local interactions contribute to the cohesive energy of the material. The crystal orbital Hamilton population (COHP) analysis, developed by Dronskowski and Blöchl in 1993, allows one to quantify the energy contribution of individual atomic bonds to the total energy of the system (the bond strength). This tool has established itself as a widely used method for understanding existing and new materials, because it provides a detailed quantitative way to establish links between the (macroscopic) properties of a material and its microscopic structure.
Lecturers

Prof. Richard Dronskowski, RWTH Aachen University (Germany)
Prof. Volker Deringer, University of Oxford (UK)
Dr. Janine George, Federal Institute for Materials Research and Testing, Berlin (Germany) and FS University Jena (Germany)
Dr. David Schnieders, RWTH Aachen University (Germany)
Peter Müller, RWTH Aachen University (Germany)

Venue
The LOBSTER School 2024 will take place in the main campus of Aalto University, in the Helsinki metropolitan area. The School’s venue is Aalto Design Factory‘s Studio, Puumiehenkuja 5A, Espoo, 02150 Finland.
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).
Schedule
This is the preliminary schedule. More detailed information will be provided later.

Tuesday 12 March
Wednesday 13 March
Thursday 14 March

9:00 – 9:30
Registration and welcome addresses (9:20 Miguel Caro & 9:25 Richard Dronskowski)

9:30 – 10:30
Chemical Bonding 101 (Richard Dronskowski)
Charges, Madelung, Bond Indices, Polarizations (Peter Mueller)
Defects, nanomaterials, amorphous matter (Volker Deringer)

10:30 – 11:00
Coffee break
Coffee break
Coffee break

11:00 – 12:30
Practical session: LOBSTER installation, first steps
Practical session: advanced features and visualization
Practical session: application of the previous session

12:30 – 14:00
Lunch break (participants pay for their own lunch – Maukas space reservation at 12:45)
Lunch break (participants pay for their own lunch – Maukas space reservation at 12:45)
Lunch break (participants pay for their own lunch – Arvo space reservation at 12:45)

14:00 – 15:00
LOBSTER nuts-and-bolts, plane waves & orbitals, projection to atomic orbitals (Daniel Schnieders)
LOBSTER advanced, projection to molecular orbitals, other basis sets, magnetism
LOBSTER automation (Janine George)

15:00 – 15:30
Coffee break
Coffee break
Coffee break

15:30 – 17:00
Practical session: basic features
Practical session: more advanced features
Practical session: application of the previous session

17:30 – 19:00
Poster session (takes place at the School of Chemical Engineering building’s upstairs lobby, Kemistintie 1)

18:30 – 20:30

Dinner @ Fat Lizard Restaurant Otaniemi

Participants

Name
Institution
Contact

Miguel Caro
Aalto University
miguel.caro@aalto.fi

Peter Müller
RWTH Aachen University
peter.mueller@ac.rwth-aachen.de

Hanwen Zhang
University of Oxford

Javier Sanz Rodrigo
DTU
sanz@dtu.dk

Rajeev Dutt
University of Warwick
rajeev.dutt@warwick.ac.uk

Linh Tong
Aalto University
linh.tong@aalto.fi

Volker Deringer
University of Oxford
volker.deringer@chem.ox.ac.uk

Wanja Schulze
University of Jena
wanja.schulze@uni-jena.de

Nityasagar Jena
Linköping University
nityasagar.jena@liu.se

Ransel Barzaga
Instituto de Astrofísica de Canarias

Scott Simpson
St. Bonaventure University
ssimpson@sbu.edu

Alyssa Santos
St. Bonaventure University

Anson Thomas
Indian Institute of Technology Roorkee
anson_t@cy.iitr.ac.in

M.D. Hashan C. Peiris
Binghamton University – State University of New York
mpeiris1@binghamton.edu

Richard Dronskowski
RWTH Aachen University

Divya Srivastava
Turku University
divya.srivastava@utu.fi

Pablo Castro Latorre
University of Barcelona
p.castrola@ub.edu

Elisa Damiani
University of Bologna
elisa.damiani4@unibo.it

Madhavi Dalsaniya
Warsaw University of Technology
madhavi.dalsaniya.dokt@pw.edu.pl

YiXu Wang
RWTH Aachen University
yixu.wang@ac.rwth-aachen.de

Edith Simmen
ETH Zurich
edith.simmen@mat.ethz.ch

Rafael Nunez
Aalto University
rafael.nunez@aalto.fi

Aleksandra Oranskaia
KAUST
aleksandra.oranskaia@kaust.edu.sa

David Schnieders
RWTH Aachen University
david.schnieders@ac.rwth-aachen.de

Neeraj Mishra
Ben-Gurion University of the Negev
neeraj@post.bgu.ac.il

Madhavi Dalsaniya
Warsaw University of Technology
madhavi.dalsaniya.dokt@pw.edu.pl

Rina Ibragimova
Aalto University
rina.ibragimova@aalto.fi

Munavvar Husain
University of Warsaw

Janine George
BAM Berlin

Online resources

LOBSTER website (for code download, etc.): http://cohp.de/
Github repository: https://github.com/davSchnieders/LOBSTERSchool
wxDragon fro visualization: Linux version; Windows version
Step-by-step procedure:

Download and unpack the LOBSTER code
Add the LOBSTER binary to your PATH: export PATH=/lobster_code_root_directory:$PATH
Clone the github repo: git clone https://github.com/davSchnieders/LOBSTERSchool.git
Download and unpack wxDragon
Make the wxDragon binary executable (from within the wxDragon directory): chmod +x wxDragon
Add the wxDragon binary to your PATH: export PATH=/wxDragon_code_root_directory:$PATH

To install LobsterPy, you can just do: pip install lobsterpy (under Ubuntu, you may also need to upgrade your requests package, e.g.: pip install requests==2.25.0)

Registration
The registration is now closed.
Local organizers

Dr. Miguel Caro, Aalto University (Finland)
Dr. Rina Ibragimova, Aalto University (Finland)
Dora Javor, Aalto University (Finland)

Sponsors
In addition to organization support by OCAMM, the LOBSTER School 2024 is financially supported by the Psi-K network, the Aalto University Deparment of Chemistry and Materials Science, and the Finnish CECAM node.

Seminar by Prof. Richard Dronskowski on the crystal structure of carbonic acid
2024-03-11 14:15 - 15:15 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Save the date! OCAMM presents a special seminar by Prof. Richard Dronskowski from RWTH Aachen, Germany on the always fascinating (and sometimes controversial!) topic of carbonic acid. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 11 March 2024 @ 14:15 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn something new (or revisit your ideas) about carbonic acid!
Title
The Crystal Structure of Carbonic Acid – A Stroll between Molecular Chemistry, Theory, High Pressure, Neutron Diffraction, and Chemical Bonding
Abstract
Ubiquitous carbonic acid, H2CO3, a key molecule in biochemistry, geochemistry, and also extraterrestrial chemistry, is known, at least in principle, from various physicochemical studies but is often considered, even up to the present day, a somewhat mysterious “non-existing” molecule. In fact, the molecule has never been directly seen, the reason being that high pressure is needed to stabilize it, as easily shown by electronic-structure theory. After an eight-years research study, the crystal structure of carbonic acid was determined from neutron-diffraction data on a deuterated sample in a specially built hybrid clamped cell using “Russian alloy”. At 1.85 GPa, D2CO3 crystallizes in the monoclinic space group P21/c with a = 5.392(2), b = 6.661(4), c = 5.690(1) Å, β = 92.66(3)°, Z = 4, with one symmetry-inequivalent anti-anti shaped D2CO3 molecule forming dimers, as previously predicted. Quantum chemistry evidences π bonding within the CO3 molecular core, very strong hydrogen bonding between the molecules, and a massive inﬂuence of the crystal ﬁeld on all bonds; phonon calculations emphasize the locality of the vibrations, being rather insensitive to the extended structure. Now that carbonic acid has been firmly established, this may be important for other fields, for example CO2 “sequestration” and its the chemical consequences. Likewise, carbonic acid probably plays a role in our solar system, say, on outer gas planets such as Uranus or Neptune and, also, on the Jupiter moon Europa. Finally, many chemistry textbooks must be rewritten because the simplest molecule consisting of water and carbon dioxide actually exists.
See also: “The Crystal Structure of Carbonic Acid”. S. Benz, D. Chen, A. Möller, M. Hofmann, D. Schnieders, and R. Dronskowski. Inorganics 10, 132 (2022).
About the speaker
Richard Dronskowski studied chemistry and physics at the University of Münster in the early 1980s. After having received his diplomas in 1987 and 1989, he got his Ph.D. in 1990 from the Technical University of Stuttgart and the Max Planck Institute for Solid State Research with the thesis “Condensed Clusters in Oxides and Arsenides of Molybdenum”. Five years later, he received both habilitation and venia legendi from the University of Dortmund.
In his professional career he worked as a visiting scientist at Cornell University and the Max Planck Institute at Stuttgart. Since 1997 he has been with RWTH Aachen University where he holds the Chair of Solid-State and Quantum Chemistry. He is also engaged at the Hoffmann Institute of Advanced Materials in Shenzhen, China.

Seminar by Prof. Janine George on automation and workflows in atomistic simulation
2024-03-11 10:00 - 11:00 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Save the date! OCAMM presents a special seminar by Prof. Janine George on automation and workflows in atomistic simulation. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 11 March 2024 @ 10:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn more about how high-throughput calculations of molecules and materials can be made efficient and tractable!
Title
Data-Driven Chemical Understanding with Bonding Analysis
Abstract
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties [1]. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available [2,3]. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale [3,4]. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials [5,6]. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed [7–10].
References

J. George, G. Hautier, Trends Chem. 2021, 3, 86–95.
D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier, Acta Cryst B 2020, 76, 683–695.
J. George, G. Petretto, A. Naik, M. Esters, A. J. Jackson, R. Nelson, R. Dronskowski, G.-M. Rignanese, G. Hautier, ChemPlusChem 2022, 87, e202200123.
“LobsterPy,” can be found under https://github.com/JaGeo/LobsterPy, 2022.
A. A. Naik, C. Ertural, N. Dhamrait, P. Benner, J. George, Sci Data 2023, 10, 610.
K. Ueltzen, A. Naik, C. Ertural, P. Benner, J. George, Article in Preparation 2024.
J. George, Trends Chem. 2021, 3, 697–699.
A. Ganose, et al., “atomate2,” can be found under https://github.com/materialsproject/atomate2, 2023.
A. S. Rosen, M. Gallant, J. George, J. Riebesell, H. Sahasrabuddhe, J.-X. Shen, M. Wen, M. L. Evans, G. Petretto, D. Waroquiers, G.-M. Rignanese, K. A. Persson, A. Jain, A. M. Ganose, Journal of Open Source Software 2024, 9, 5995.
I. Batatia, P. Benner, Y. Chiang, A. M. Elena, D. P. Kovács, J. Riebesell, X. R. Advincula, M. Asta, W. J. Baldwin, N. Bernstein, A. Bhowmik, S. M. Blau, V. Cărare, J. P. Darby, S. De, F. Della Pia, V. L. Deringer, R. Elijošius, Z. El-Machachi, E. Fako, A. C. Ferrari, A. Genreith-Schriever, J. George, R. E. A. Goodall, C. P. Grey, S. Han, W. Handley, H. H. Heenen, K. Hermansson, C. Holm, J. Jaafar, S. Hofmann, K. S. Jakob, H. Jung, V. Kapil, A. D. Kaplan, N. Karimitari, N. Kroupa, J. Kullgren, M. C. Kuner, D. Kuryla, G. Liepuoniute, J. T. Margraf, I.-B. Magdău, A. Michaelides, J. H. Moore, A. A. Naik, S. P. Niblett, S. W. Norwood, N. O’Neill, C. Ortner, K. A. Persson, K. Reuter, A. S. Rosen, L. L. Schaaf, C. Schran, E. Sivonxay, T. K. Stenczel, V. Svahn, C. Sutton, C. van der Oord, E. Varga-Umbrich, T. Vegge, M. Vondrák, Y. Wang, W. C. Witt, F. Zills, G. Csányi, 2023, DOI 10.48550/arXiv.2401.00096.

About the speaker
Janine George received her Bachelor of Science in Chemistry and Master of Science (summa cum laude) also in Chemistry, both from RWTH Aachen University, in 2011 and 2013, respectively. She then obtained a Doctorate (Dr. rer. nat, summa cum laude) in Computational Solid-State Chemistry under the supervision of Prof. Richard Dronskowski, RWTH Aachen University in 2017. During 2018-2021 she held a Post-Doc position in the groups of Prof. Geoffroy Hautier at the Université catholique de Louvain (now at Darthmouth College) and Prof. Gian-Marco Rignanese also at the Université catholique de Louvain. Since 2021 she is Junior Group Leader of the Group “Computational Materials Design” at the Federal Institute for Materials Research and Testing (Department Materials Chemistry) in Berlin and, since 2023, she holds a joint appointment as Professor for Materials Informatics between the latter and the FSU Jena (Institute of Condensed Matter Theory and Optics).

Seminar by Tamas Stenczel on machine-learning accelerated ab-initio molecular dynamics
2024-02-07 14:15 - 15:15 UTC | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
Save the date! OCAMM presents a special seminar by Tamas Stenczel on accelerating ab initio molecular dynamics with on-the-fly machine learning. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 7 February 2024 @ 14:15 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi to learn more about how you can make molecular dynamics orders of magnitude faster while retaining ab initio accuracy!
Title
ML Acceleration for Dynamics in Ab-Initio Modelling
Abstract
Showcase of existing methods and ongoing work on accelerating various dynamical simulations in ab-initio codes with modern ML. Replacing the ab-initio energy model where possible with an on-the-fly updated ML (GAP/ACE/MACE) one, we will visit methods and frameworks making materials science & chemical ML models easy to access for all modelling needs. Next generation methods, frameworks, scientific challenges, and opportunities for using and contributing to efforts in the field will be presented, starting from recently published advances in the CASTEP code, going to a general framework currently being implemented in a range of simulation tools.
About the speaker
Tamás K. Stenczel is a researcher at the University of Cambridge, with ties in academia and in various industrial realms. Amongst heading a research and software team at a family office, conducting open academic research, and everyday life, he is a skiier and biker, always striving to see and understand more of the world.

Seminar by Prof. Rocío Mercado on artificial intelligence in biomolecular modeling
2023-11-13 14:00 - 15:00 (Europe/Helsinki) | Aalto University, School of Chemical Engineering, Kemistintie 1, Kemistintie 1, Espoo, 02150, Finland
OCAMM presents a special seminar by Prof. Rocío Mercado from Chalmers University of Technology, Sweden on “Deep generative models for biomolecular engineering“. This invited seminar will take place at the Department of Chemistry and Materials Science, Aalto University on 13 November 2023 @ 14:00 in lecture hall A304 (Ke2) at the main building of the School of Chemical Engineering in the Otaniemi campus, Kemistintie 1, 02150 Espoo. The seminar is open to all. Please join us in Otaniemi for a great talk on how artificial intelligence and molecular modeling are impacting the fields of biomolecular design and drug discovery.
Title
Deep generative models for biomolecular engineering
Abstract
AI is transforming our approach to molecular engineering. Driven by the goal of accelerating drug development, our aim is to develop AI-driven molecular engineering methods which will enhance our approach to biomolecular discovery, such as drug discovery, drug repurposing, and chemical probe identification. This entails the development of generative and predictive tools that can learn from biochemical data, such as molecular structures, chemical reactions, and biomedical data. While AI can be applied to a range of molecular engineering tasks, one ideal area is de novo molecular design. De novo design is the concept of designing molecules with desired properties from scratch so as to minimize experimental screening, and is poised to allow scientists to more efficiently traverse chemical space in search of optimal molecules, and delegate error-prone decisions to computers via the use of predictive and generative models. In drug development, de novo design methods can aid medicinal chemists in the design and selection of drug candidates, with the added advantage that they can learn from datasets of billions of molecules in minutes and be constantly updated with new data. Deep molecular generative models are a particular approach to de novo design which uses deep neural networks to generate new molecules in silico, and works by proposing atom-by-atom (or fragment-by-fragment) modifications to an initial graph structure to generate compounds predicted to achieve a certain property profile. Such models can be applied to a range of therapeutic modalities.
In this talk, I will discuss the development of deep generative models for various molecular engineering tasks relevant to early-stage drug discovery. These include a model for synthesizability-constrained molecular generation, a reinforcement learning framework for molecular graph optimization, and recent applications from our group to the design of large modalities for targeted protein degradation.
About the speaker
Rocío is a tenure-track assistant professor in the Data Science and AI division at Chalmers since January 2023. She heads the AI Laboratory for Biomolecular Engineering (AIBE) in the Department of Computer Science and Engineering.
Previously, she was a postdoctoral associate in the Coley group at MIT, as well as an industrial postdoc in the Molecular AI team at AstraZeneca. Throughout her postdoctoral career, she worked on the development of deep generative models for small molecule drug discovery. Before AstraZeneca, she was a PhD student in Professor Berend Smit’s molecular simulation group at UC Berkeley and EPFL. She received her PhD in Chemistry from UC Berkeley in August 2018, and her BSc in Chemistry from Caltech in June 2013.

Machine Learning Interatomic Potential School for Young & Early Career Researchers
2023-11-06 - 2023-11-10
For up-to-date and more complete information, please visit the official website: https://www.mlip-workshop.xyz/home

MLIP 2023 is a CECAM & Psi-k hybrid school aimed at young and early-career researchers who are interested in using machine learning interatomic potentials (MLIP) in their research. The two main goals of this school are:

to give researchers a solid introduction to the basic scientific techniques of designing, fitting, and validating MLIPs for chemical/material systems

to provide a platform for those interested in using MLIPs to connect with those involved in MLIP development to accelerate the adoption of ML techniques in the wider atomistic simulation community

MLIP 2023 is the second edition of the MLIP 2021 workshop, and will physically take place at Aalto University in Espoo, Finland, between 06–10 November 2023. Remote, online participation is also possible.
The school will consist of keynote lectures on different topics of MLIP as well as hands-on tutorials that will allow the participants to apply the explained concepts to relevant toy cases and also their own research. The invited speakers are leading scientists in the field of MLIP, at various career stages, who are well–equipped to share their experience with those getting started in the field.
Invited speakers

Alice Allen, Los Alamos National Laboratory, USA
Nongnuch Artrith, Utrecht University, the Netherlands
Jörg Behler, Ruhr-Universität Bochum, Germany
Michele Ceriotti, École polytechnique fédérale de Lausanne, Switzerland
Rose Cersonsky, University of Winsconsin-Madison, USA
Cecilia Clementi, Freie Universität Berlin, Germany
Zheyong (Bruce) Fan, Bohai University, China and Aalto University, Finland
Guillaume Fraux, École polytechnique fédérale de Lausanne, Switzerland
Andrea Grisafi, École Normale Supérieure, France
Dávid Kovács, University of Cambridge, UK
Chris Pickard, University of Cambridge, UK
Martin Uhrin, Université Grenoble Alpes, France
Sander Vandenhaute, Ghent University, Belgium
Chuck Witt, University of Cambridge, UK
Linfeng Zhang, AI for Science Institute and DP Technology, China

Schedule
Please visit https://www.mlip-workshop.xyz/schedule for an up-to-date schedule.
Registration
Please see https://www.mlip-workshop.xyz/participate for detailed instructions. However, applications should still be submitted via the CECAM website (using the “Participate” tab). The registration deadline is 22 September 2023.
Venue
Aalto University, Finland. Check https://www.mlip-workshop.xyz/practical-info for more details on practicalities.
Organizers

Chiheb Ben Mahmoud, University of Oxford (UK)
Miguel Caro, Aalto University (Finland)
Sanggyu Chong, EPFL (Switzerland)
Federico Grasselli, EPFL (Switzerland)
Kevin Kazuki Huguenin-Dumittan, EPFL (Switzerland)
Venkat Kapil, University of Cambridge (UK)
Felix-Cosmin Mocanu, École Normale Supérieure (France)
Jigyasa Nigam, EPFL (Switzerland)
Davide Tisi, EPFL (Switzerland)
Max Veit, Aalto University (Finland)

Sponsors
