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
Group photo taken on the Campus Joseph Aiguier, Marseille.
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.
One of the panel discussions in a hybrid format.
Detailed programme
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
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
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
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
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
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
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
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
The Recent Advances in Computer-aided X-ray Spectroscopy (RACXS 2024) workshop took place on 17-20 June at Aalto University, Finland. The event took place at the Department of Chemistry and Materials Science and was locally organized by Miguel Caro, Tigany Zarrouk and Patrick Rinke, and was financially supported by Psi-K, the Finnish CECAM node, the Aalto University Science Institute and the Aalto University Department of Chemistry and Materials Science.
The workshop gathered circa 60 participants from various countries (especially from Europe) to discuss about the current state of art and trends in computational approaches to predicting and interpreting X-ray spectroscopy of molecules and materials. The workshop was not limited to computational experts but also featured a good representation of experimentalists eager to use and curious about how to use novel computer-based methodologies to undertand the link between X-ray spectra and the atomic-scale structure of molecules and materials.
As expected, machine learning featured prominently in this workshop, but we also discussed developments in electronic structure methods for core-level prediction. In addition to these, the most prominent themes of the workshop were on deconvolution of X-ray spectra and incorporation of experimental observables into computational workflows.
From 10th to 14th June 2024 the second conference devoted to first-principles calculations of defect qubits’ magneto-optical and spin properties for quantum technologies was held at the Eötvös Loránd University (ELTE) in Budapest, Hungary where theory also met experiments to discuss scientific issues.
This conference welcomed 154 participants from 28 countries registered for the workshop from 5 continents including the organizers. The participants showed up in person at the workshop site and attended the scientific talks except for one attendee with on-line participation. The final scientific program of the workshop lasted for five full days, included 18 invited talks, 41 contributing talks, and a poster session with 87 posters, and a Discussion session about single defect engineering with leading experimentalists and theorist. The event was sponsored jointly by the Psi-k organization, CECAM HQ, Eötvös Loránd University (Budapest, Hungary), Quantum Information National Laboratory of Hungary, and the Applied Physics Letters. Continue reading Defects in solids for quantum technologies – 2024→
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.
Canola Sofia(Institute of Physics of the Czech Academy of Sciences)
Saravanabavan Karthikeyan (CEA-Liten, University of Grenoble)
Invited speakers
Castellano Aloïs(University of Liège)
Filip Maria-Andreea(Max Planck Institute for solid state research)
Giarrusso Sara(University of Paris-Saclay)
Janke Svenja M.(University of Warwick)
Levi Gianluca (University of Iceland)
Mejuto-Zaera Carlos(International School for Advanced Studies, SISSA)
Nys Jannes(École Polytechnique Fédérale de Lausanne)
Schaefer Julia M. (Heliatek GmbH)
Urquiza Laura(École Polytechnique)
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.
Objectives: The aim of the school was to give a comprehensive introduction to the theoretical and practical aspects of the electronic excitations that are probed by experimental techniques such as optical absorption, EELS and photoemission (direct or inverse). From a theoretical perspective, excitations and excited state properties are out of the reach of density-functional theory (DFT), which is a ground-state theory. Over the past three decades, alternative ab-initio theories and frameworks capable of describing electronic excitations and spectroscopy, have gained popularity including time-dependent density-functional theory (TDDFT) and many-body perturbation theory (MBPT) or Green’s function theory (GW approximation and Bethe-Salpeter equation BSE). In fact, computational solutions and codes have been developed to implement these theories and to provide tools to calculate excited state properties. The present school focused on these points, covering theoretical, practical, and also numerical aspects of TDDFT and MBPT, non-linear response and real-time spectroscopies. Additionally, the school provided valuable insights from an experimental perspective on spectroscopies and magnetic excitations, rarely covered in similar events. Finally, a large part of the school was devoted to getting familiar with the codes that implement such theories (ABINIT, 2Light, Lumen, DP, and EXC). Continue reading Report for Theoretical Spectroscopy Lectures 2024→
Held at CECAM-HQ, EPFL, Lausanne, February 19-24, 2024
This workshop continues the successful trajectory of the CECAM Electronic Structure Library initiative, aimed at supporting and connecting developers of shared libraries and software tools in support of electronic structure simulations across our entire community. As always, CECAM’s professionalism and remarkable organization, as well as their truly welcoming hospitality, deserve the highest praise.
Background
Shared computational libraries that provide key functionality are now firmly established parts of the electronic structure software ecosystem. As electronic structure methods and codes diversify and mature, the development of libraries strengthens collaborations and avoids reimplementing the same methods in the context of a different, monolithic code. Over the years, a modular paradigm has emerged in which central pieces can be shared and reused between different projects. Continue reading CECAM Flagship Workshop: Electronic Structure Software Development: Advancing the Modular Paradigm→
The LOBSTER School on Chemical Bonding Analysis took place at Aalto University, Finland on 12-14 March 2024 and gathered over 30 participants. The purpose of the School was to introduce the theory and practicalities, as well as recent developments on both, behind the LOBSTER code. LOBSTER is a popular code that allows the user to perform “electronic structure reconstruction” in terms of localized projections of plane-wave-based wavefunctions, allowing a quantitative interpretation of the nature of chemical bonding in solids.
Machine learning interatomic potentials (ML-IPs) have now established themselves as a key technique in atomistic modeling. They allow the simulation of many diverse types of systems, from the molecular to the solid state, at the accuracy of highly sophisticated electronic structure methods but at a greatly reduced cost. While the general methodology of training and validating a machine learning potential has been well established, many codes and integrated software applications exist to perform these tasks. Since many of these come with a high entry barrier, there is still a need to educate young and early-career researchers in these tasks, as well as provide a pathway to enter the field and make valuable contributions for researchers who have promising ideas that could benefit from the application of ML-IPs.
This summer school targeted an audience consisting of PhD students and young postdocs, industry-based researchers as well as researchers from countries without tier 0 supercomputing facilities. There were 45 participants, among which 9 ladies. The school covered a wide range of topics to show the challenges and opportunities of exascale computing in ab initio materials science. Lectures provided in-depth information on the fundamentals of advanced exchange-correlation functionals, many-body perturbation theory based on Green functions, and coupled-cluster method applied to solids. Special focus was on libraries and software applications developed in the NOMAD Center of Excellence, for which training was provided, including on LUMI, https://www.lumi-supercomputer.eu/, a powerful pre-exascale European Union high-performance computer. Fundamentals and recent developments in the field were presented by recognized experts, and there was plenty of room for open exchange between the young scientists and established international experts. Continue reading Summer School “Towards exascale solutions in Green function methods and advanced DFT” Paphos, Cyprus, October 3-8, 2023→
Ab initio (from electronic structure) calculation of complex processes in materials
