“Shining light on matter through interdisciplinary theoretical  approaches”

3-7 June 2024, Marseille (France)

Event website

The 27th edition of the ETSF Workshop was distinguished by its interdisciplinary focus. It served as an open forum to discuss various emerging topics such as ultrafast phenomena via non-equilibrium dynamics, attosecond time-resolved vibrational and electronic spectroscopy, and charge and energy transfer in solids, hetero- and nanostructures, including the role of point and extended defects and thermal effects. The development and application of the most advanced methods in the description of electronic excitations, such as multiscale and embedding methods as well as theoretical and numerical developments, have enriched the discussion.

The main topics were:

• Non-equilibrium dynamics
• Charge and energy transfer in solids, in hetero- and nano- structures
• Advanced methods in the description of electronic excitations
• Theoretical and numerical developments

Continue reading 27th ETSF Workshop on Electronic Excitations →

Held at Zuse Institute, Berlin, July 8-12, 2024

On 8-12 of July 2024, we gathered at the CECAM node at the Zuse Institute Berlin to delve into the topic of Machine Learning of First Principles Observables. Seventy-five participants travelled to attend the event in person and nearly one hundred registered to join remotely. The event was jointly sponsored by CECAM, the Psi-k Charity, Deutsche Forschungsgemeinschaft, and the Max-Planck-Gesellschaft.

The workshop addressed the growing need for models, workflows, and databases that go beyond the established methods of producing machine learning (ML) interatomic potentials and serve to predict experimentally observable quantities. During the event, we addressed the topic in eight subject-specific sessions, each consisting of four talks and a panel discussion, which covered topics from “Thermodynamic observables” to “Long-range interactions” and “Spectroscopy” and considered the future advancements of the field. The invited and contributed speakers came from a range of career stages, from both theoretical and experimental backgrounds.

Main takeaways

The eight sessions of the workshop were focusing on the following topics:

– Thermodynamic Observables
– Electronic Structure and Long-Range Interactions (3 sessions)
– Magnetic Observables
– Spectroscopic Observables (2 sessions)
– Databases and Reaction Networks

Overarching all sessions, several topics were identified to be very important in forming this community:

Data Sharing and Management: In almost every panel discussion, the importance of effective data sharing, meta-data utilisation, and the creation and maintenance of curated databases was discussed. It was also emphasised that these databases should also include negative results, which further constrain the ML models and make them more robust. These data are critical for experimentally relevant ML models for the future. The importance of code documentation and reproducibility was also highlighted during the discussions.

Bridging Experiment and Simulation: This workshop served as a springboard for facilitating exchange between theoreticians and experimentalists. By encouraging discussion between both groups, the speakers and participants identified several areas where these two groups could bridge the multi-scaling gap from both ends. This involves theoreticians reconsidering the approximations and simplifications in their models to make them more realistic by incorporating factors such as interfaces and defects. At the same time, experimentalists were encouraged to conduct repeated experiments on less complex model systems whose simulations are more attainable for the current computational approaches. This dual approach aims to bring theory and experiment closer together, bridging the complexity gap from both ends.

Metrics for Evaluating Predicted Data: The final topic that emerged during the workshop was the need for better metrics for evaluating the accuracy of the predicted data beyond simple scalar values. The discussion covered metrics which allow for tolerance in variations in spectra shifts, peak width, and spectral intensities. Additionally, the importance of general and foundational ML models such as MACE-MP-0, ChargNET and AIMNet2 , as well as the need for benchmarking them in more realistic atomistic modelling tasks, was highlighted.

Workshop format

Each of the morning or afternoon sessions consisted of an invited overview talk, three invited or contributed talks, and a panel discussion led by the session chair. While the oral presentations are essential to every workshop, the addition of generous panel discussions allowed for some time to reflect, debate, and elucidate on the session topics in the bigger picture, and also engaged a good number of the audience members. The discussions continued throughout the week during the poster session on Monday, workshop dinner on Wednesday and walking tours on Thursday, allowing for the participants to connect in a less formal setting.

It was important to us to open the workshop up for remote participation, to reach and include those we could not accommodate due to capacity limitations or who could not join onsite. The most significant remote aspect was streaming the talks over Zoom where around 50 participants joined each of the sessions.

Detailed programme

1. Thermodynamic Observables
   • Prof. Dr. Karsten Reuter: Out of the Crystalline Comfort Zone: Tackling Working Interfaces with Machine Learning
   • Dr. Michele Simoncelli: Machine learning opens a wonderland for looking through glasses
   • Dr. Christian Carbogno: Accelerating Transport Coefficient Predictions via Machine Learning
   • Prof. Dr. Nong Artrith: Harnessing Machine Learning for Advancing Amorphous Battery Materials
2. Electronic Structure & Long-Range Interactions I
   • Prof. Dr. Gábor Csányi: A foundational atomistic model for materials
   • Prof. Dr. Janine George: High-throughput Approaches for Materials Understanding and Design
   • Sergey Pozdnyakov: Challenging the dogma of rotational equivariance in atomistic ML
   • Prof. Dr. Kulbir Ghuman: Leveraging Computational Advances to Design and Optimize Energy Materials: From Traditional Methods to Machine Learning
3. Electronic Structure & Long Range Interactions II
   • Prof. Dr. Michele Ceriotti: Machine-learning for electronic structure
   • Alexander Knoll: Advanced Software Frameworks for Describing Local and Non-Local Interactions in High-Dimensional Neural Network Potentials
   • Prof. Dr. Reinhard Maurer: Electronic Structure Surrogate Learning for Quantum Dynamics and Inverse Design
   • William Baldwin: ML Electrostatics Models in Relevant Test Systems
4. Magnetic Observables
   • Prof. Dr. Stefano Sanvito: The Jacobi-Legendre framework for materials discovery
   • Johannes Wasmer: Prediction of magnetic exchange interaction in doped topological insulators
   • Prof. Dr. Alessandro Lunghi: Machine Learning for Molecular Magnetism
   • Shuping Guo: Machine learning facilitated by microscopic features for discovery of novel magnetic double perovskites
5. Spectroscopic Observables I
   • Prof. Dr. Patrick Rinke: Machine Learning for Spectroscopy – Concepts, Successes, and Challenges
   • Dr. Tigany Zarrouk: Experiment-driven atomistic materials modeling: Combining XPS and MLPs to infer the structure of a-COx
   • Prof. Dr. Rose Cersonsky: Categorizing three-dimensional photonic crystals: open challenges in scale-covariant problems
   • Clelia Middleton: p-DOS: a descriptor with electronic wisdom for learning X-Ray spectroscopy
6. Spectroscopic Observables II
   • Prof. Dr. Rebecca Nicholls: Interpreting core-loss spectroscopy
   • Prof. Dr. Josef Granwehr: Predicting electron paramagnetic resonance parameters and their sensitivity to structural configuration
   • Prof. Dr. Claudia Draxl: Assessing spectroscopic features: from fingerprinting to predictions
   • Prof. Dr. Stefan Sandfeld: Scientific Machine Learning and Explainable AI Approaches for the Physical Sciences
7.  Electronic Structure & Long-Range Interactions III
   • Luca Leoni: Machine learned small polaron dynamics
   • Bartosz Brzoza: Applying SE(3)-Equivariant Attentional Graph Neural Networks for the purpose of predicting the electronic structure of molecular hydrogen
8. Databases & Reaction Networks
   • Prof. Dr. Johannes Margraf: Machine Learning in Chemical Reaction Space
   • Dr. Jonathan Schmidt: Alexandria database: All you need is more data in material science?
   • Prof. Dr. Olexandr Isayev: Scaling Molecular Modeling to Millions of Reactions with Neural Network Potentials
   • Dr. Pierre-Paul De Breuck: Property predictions from limited and multi-fidelity datasets

Organizers

• Simone Köcher (IET-1, Forschungszentrum Jülich GmbH)
• Angela Harper (Fritz-Haber Institut der Max Planck Gesellschaft)
• Hanna Türk (École Polytechnique Fédérale de Lausanne)
• Elena Gelžinytė (Fritz-Haber Institut der Max Planck Gesellschaft)
• Giulia Glorani (Fritz-Haber Institut der Max Planck Gesellschaft)

Further details

• Workshop booklet;
• Workshop website, which includes the abstracts and the full list of participants.

27-31 May 2024, Toulouse, France 

From the 27th to the 31st of May at the University of Paul Sabatier III in Toulouse, France, the ETSF-YRM workshop was held. In particular the newly built Fermi Building hosted the event in the heart of the university complex. The event gathered 51 people from all over Europe. The event was sponsored by the Psi-K organization, NanoX and the CECAM nodes of Toulouse and Grenoble.

The objective of this meeting is to provide young researchers with the opportunity to share their work and acquaint themselves with state-of-the-art theoretical methods applied both in their own field and in others. Moreover, it offers scientists at the beginning of their careers the chance to network with young colleagues from different institutions, exchange knowledge and ideas and thus integrate further into the scientific community. The event was thought to be welcoming and inclusive, and an enriching experience for young researchers who may not have participated in many conferences before. The aim was also to provide a less intimidating setting than a large-scale international conference. In order to achieve all the above, the organizers provided a friendly and comfortable atmosphere and prioritized the early stage researchers in the oral sessions, helping them develop their presentation skills. The organization committee made sure that all applicants had equal opportunities. The YRM 2024 was divided into five oral sessions. The first session was on advanced electronic structure methods with a particular focus on spectroscopic properties. The second session was  on optical properties of materials. The third session was on the vibrational properties of materials. The fourth session was on strongly correlated systems and the fifth session was focused on machine learning in condensed matter. For each session, one or two invited speakers gave an overview of the state-of-the-art in their field and presented their work as well in a keynote talk. 

The workshop also organized a poster session and a social dinner for participants to get to know each other and discuss their current research projects.

Detailed program and book of abstracts

1.Detailed program

2.Book of abstracts

Organizers 

1. Ammar Abdallah (LCPQ, Université Toulouse III )
2. Biancorosso Leonardo (University of Trieste)
3. Canola Sofia (Institute of Physics of the Czech Academy of Sciences)
4. Saravanabavan Karthikeyan (CEA-Liten, University of Grenoble)

Invited speakers 

1. Castellano Aloïs (University of Liège)
2. Filip Maria-Andreea (Max Planck Institute for solid state research)
3. Giarrusso Sara (University of Paris-Saclay)
4. Janke Svenja M. (University of Warwick)
5. Levi Gianluca (University of Iceland)
6. Mejuto-Zaera Carlos (International School for Advanced Studies, SISSA)
7. Nys Jannes (École Polytechnique Fédérale de Lausanne)
8. Schaefer Julia M. (Heliatek GmbH)
9. Urquiza Laura (École Polytechnique)
10. Vanzan Mirko (University of Milano)

General remarks

Overall, the sessions offered a comprehensive overview of advancements in theoretical spectroscopy. The participants showcased new methodological approaches in materials and molecular sciences, including theoretical models, computational methods, and applications of artificial intelligence, alongside improvements in existing techniques. Additionally, spectroscopic properties such as optical and vibrational characteristics were discussed for novel systems of interest, including solids, molecules, and hybrids.

As experimental research in the field advances and the precision of descriptive models improves, the primary constraint remains computational feasibility. This highlights ongoing questions about how to simulate increasingly complex and large-scale systems for more accurate interpretations of experimental results.

Conclusion and prospects

The workshop was very succesfull and entartaining . All the invited speakers excelled in crafting and presenting their work. The participants responded with great enthusiasm and energy, significantly contributing to the success of this workshop. We gratefully thank Amandine Laurient who works at LCPQ in Toulouse, without her there wouldn’t be any workshop.