Artificial intelligence (AI) is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks (ANNs) that are many layers deep and can often reproduce human-like behavior much better than other machine-learning techniques.

This development has been going on for a long time, and it is ambiguous to identify a specific starting date for it. Possible candidates might be the first ANNs in 1943 (McCulloch & Pitts, 1943), the popularization of backpropagation to train many layers in a network at once in 1985 (Rumelhart et al., 1986) and the application to identify human handwriting in 1989 (Le Cun et al., 1989).

Today the growth of this field is seemingly exponential, with major breakthroughs being reported almost every year. Some of the most notable examples are

(1) beating a professional player at the game of go (Silver et al., 2016),

(2) residual neural networks (ResNets) for image recognition (He et al., 2016),

(3) AlphaFold protein structure prediction (Jumper et al., 2021),

(4) ChatGPT and other large language models (LLMs) (https://gluebenchmark.com/leaderboard).

During the same time frame, deep learning has also seeped into our society in many less prominent cases such as

(1) automatic image processing of photographs from our cell-phone cameras,

(2) recommendations for music or movies on streaming services,

(3) optimization of server hall power efficiency.

It is certainly no understatement that AI has become a foundation for society as we know it today. And so far, the technology has mainly been developed by big tech companies.

With this knowledge, many scientists have started to investigate what these new tools could mean for their field of research. While some areas of science were fairly slow to adopt these methods, we are now seeing a rapid spread of AI applications in many different fields, and the contributions in this collection of articles from Journal of Applied Crystallography (https://journals.iucr.org/special_issues/2024/ANNs) manifest that observation for X-ray photon science and crystallography.

Most applications of AI are, however, not of the transformative kind, comparable to the effect that AlphaFold had on the field of structure prediction. Instead, we have examples such as preprocessing that gives smaller improvements to data quality, or automation of a mundane task that would otherwise have to be done by a researcher.

Classification. One of the first applications of machine learning was to classify data. Famous examples include recognizing handwritten digits and letters or identifying human faces. In photon science this is also a common use case, and recently we have, for example, seen it being used for femtosecond X-ray imaging patterns (FXI) (Assalauova et al., 2022), X-ray photon correlation spectroscopy (Timmermann et al., 2022) and serial femtosecond crystallography (Rahmani et al., 2023). This is often a convenient way to speed up a researcher’s work by automating an otherwise labor-intensive task, by classifying a small set of patterns by hand and then training a machine-learning algorithm to classify a bigger dataset in a similar fashion.

Another example is from neutron diffraction (Hao et al., 2023) where a neural network approach was used not only to filter out relevant data but also to label regions of interest in each diffraction pattern.

A common critique towards neural network approaches is their black-box nature — that we usually have very limited understanding of the analysis that a trained network actually performs. A particularly interesting development is, therefore, the attempts to understand the internal workings of our networks. One such example is reported by Nawaz et al. (2023).

Data analysis. A more recent development is that neural networks take a more active role in data analysis in addition to classification. We see cases in the entire pipeline of data processing. A successful example is data preprocessing where, for example, missing regions of data can be recreated using deep neural networks. This has been shown for both small/wide-angle X-ray scattering (SAXS/WAXS) (Chavez et al., 2022) and FXI (Bellisario et al., 2022) experiments.

It is still fairly rare for neural networks to perform the bulk of the analysis. One such case is, however, a study of band gap structures in metal–organic frameworks where an ANN trained on a large set of simulated data was applied to experimental data with remarkable success (Gómez-Peralta et al., 2022). In another example by Lim et al. (2023), subsurface temperatures are estimated during laser melting comparing experimental data with ANN-based simulations.

Parameter estimation. A more common application is the use of neural networks to provide parameters to an analysis that is otherwise using conventional methods or to reduce the search space for an existing algorithm. Chitturi et al. (2021) use neural networks to estimate lattice parameters for X-ray powder diffraction data, which can reduce the search space for this time-consuming algorithm by more than a factor of 100. A similar example comes from X-ray reflectivity, where a neural network was used to estimate model parameters in a fraction of the time required for traditional methods (Mareček et al., 2022). Recently, a web-service called CrystalMELA was also released, which can estimate the crystal system using several machine-learning methods (Corriero et al., 2023).

In addition to direct applications, AI is mentioned by a vast number of articles, and it is clear that there is a curiosity and optimism about AI techniques within the field. The implementation of AI techniques is, however, most likely in its infancy. Most examples included here are the work of small talented teams of researchers using relatively modest hardware. The effects are already tangible, by speeding up research, identifying hidden patterns and freeing up time for researchers by automating tasks. Given the success so far, more effort and more computational power could potentially have the same transformative effect in our field as AlphaFold has had on protein structure prediction.

References

Assalauova, D., Ignatenko, A., Isensee, F., Trofimova, D. & Vartanyants, I. A. (2022). J. Appl. Cryst. 55, 444–454.

Bellisario, A., Maia, F. R. N. C. & Ekeberg, T. (2022). J. Appl. Cryst. 55, 122–132.

Chavez, T., Roberts, E. J., Zwart, P. H. & Hexemer, A. (2022). J. Appl. Cryst. 55, 1277–1288.

Chitturi, S. R., Ratner, D., Walroth, R. C., Thampy, V., Reed, E. J., Dunne, M., Tassone, C. J. & Stone, K. H. (2021). J. Appl. Cryst. 54, 1799–1810.

Corriero, N., Rizzi, R., Settembre, G., Del Buono, N. & Diacono, D. (2023). J. Appl. Cryst. 56, 409–419.

Gómez-Peralta, J. I., Bokhimi, X., García-Peña, N. G., Quintana-Owen, P. & Rodríguez-Gattorno, G. (2022). J. Appl. Cryst. 55, 1538–1548.

Hao, Y., Feng, E., Lu, D., Zimmer, L., Morgan, Z., Chakoumakos, B. C., Zhang, G. & Cao, H. (2023). J. Appl. Cryst. 56, 519–525.

He, K., Zhang, X., Ren, S. & Sun, J. (2016). 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, https://doi.org/10.1109/CVPR.2016.90. Piscataway: IEEE.

Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowie, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M., Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P. & Hassabis, D. (2021). Nature, 596, 583–589.

Le Cun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I., Henderson, D., Howard, R. E. & Hubbard, W. (1989). IEEE Commun. Mag. 27, 41–46.

Lim, R. E., Mukherjee, T., Chuang, C., Phan, T. Q., DebRoy, T. & Pagan, D. C. (2023). J. Appl. Cryst. 56, 1131–1143.

Mareček, D., Oberreiter, J., Nelson, A. & Kowarik, S. (2022). J. Appl. Cryst. 55, 1305–1313.

McCulloch, W. & Pitts, W. (1943). Bull. Math. Biophys. 5, 115–133.

Nawaz, S., Rahmani, V., Pennicard, D., Setty, S. P. R., Klaudel, B. & Graafsma, H. (2023). J. Appl. Cryst. 56, 1494–1504.

Rahmani, V., Nawaz, S., Pennicard, D., Setty, S. P. R. & Graafsma, H. (2023). J. Appl. Cryst. 56, 200–213.

Rumelhart, D., Hinton, G. & Williams, R. (1986). Nature, 323, 533–536.

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. & Hassabis, D. (2016). Nature, 529, 484–489.

Timmermann, S., Starostin, V., Girelli, A., Ragulskaya, A., Rahmann, H., Reiser, M., Begam, N., Randolph, L., Sprung, M., Westermeier, F., Zhang, F., Schreiber, F. & Gutt, C. (2022). J. Appl. Cryst. 55, 751–757.

 

This article was originally published in J. Appl. Cryst. (2024). 57, 1–2.

An overview of a related virtual collection of AI/machine-learning papers that have been published in Acta Crystallographica (Sections A, B and D), IUCrJ and Journal of Synchrotron Radiation can be found here. 

IUCr Poster Prize Awardees, from left: Shivani Grover (photo courtesy of Claire Hobday), Daniel Rainer and Cicely Tam.

Three IUCr Journals Poster Prizes were awarded at the annual conference of the British Crystallographic Association (BCA), held at the University of Leeds, UK, just before Easter 2024. The judges were looking for the three best posters from an undergraduate, postgraduate or postdoctoral researcher with up to three years' experience in distinct areas of crystallography such as structural biology, structural chemistry and applied crystallography. The judging panel was chaired by Dr Briony Yorke (University of Leeds). The prize winners were as follows, in alphabetical order:

Dr Shivani Grover, a Postdoctoral Research Associate at the University of Edinburgh, UK, whose poster was entitled “Understanding the barocaloric effect in choline based plastic crystals from ab initio molecular dynamics”.

Dr Daniel Rainer, Specialist Technician for Electron Diffraction at the National Electron Diffraction Facility, University of Southampton, UK, whose poster was entitled “The National Electron Diffraction Facility – what, where, and how for 3DED now”.

Cicely Tam, an Industrial Placement Student at Diamond Light Source, Didcot, UK, whose poster was entitled “Optimising and automating microcrystallisation methods for serial crystallography”.

The prize comprised a copy each of the IUCr’s Little Dictionary of Crystallography 3rd edition, a complimentary three-year membership of the IUCr Associates Programme and a certificate.

 

The IUCr would like to take this opportunity to thank the previous Chair, Professor John Helliwell, for filling this role so enthusiastically and to Dr Yorke for taking over the task.

Figure 1. Overview of the scanning transmission electron microscopy (STEM) serial electron diffraction workflow presented by (and adapted from) Hogan-Lamarre et al. (2024).

From materials characterization to the study of biological materials, electron crystallography has played a key role in the structural sciences (Zou et al., 2011; Glaeser, 2007). The wider field of electron crystallography has always been driven forward by advances in methodology. In recent years, electron crystallographic methods that make use of three-dimensional micro- and nanocrystals have come to the forefront (Nannenga & Gonen, 2019; Gemmi et al., 2019). Currently, the most common method of data collection is continuous rotation of the crystal in the electron beam, which produces electron diffraction datasets analogous to single-crystal X-ray diffraction datasets (Nannenga et al., 2014; Cichocka et al., 2018; Gemmi et al., 2015). This robust approach for continuous rotation electron diffraction data collection has led to an explosion in popularity of the technique and it has been successfully applied to a variety of samples including nanomaterials, organic molecules, pharmaceuticals, peptides and proteins.

Despite growing interest in the field, there are portions of the data collection pipeline that are the focus of continued methods development. For example, when studying samples that are beam sensitive and require large amounts of data to be collected, structure solution can prove to be difficult. Therefore, improvements to data collection throughput, automation of the data collection process and minimizing the exposure of crystals during collection of diffraction datasets are areas of interest.

In this issue of IUCrJ, Hogan-Lamarre et al. (2024) directly address these issues by utilizing scanning transmission electron microscopy (STEM) to rapidly collect serial electron diffraction patterns with low dose to determine high-resolution structures of nanomaterials. This work builds on previous serial electron diffraction methodology that made use of a customized STEM setup and hybrid pixel detector to determine the structures of model proteins from nanocrystals (Bücker et al., 2020). A major benefit of this new method (Fig. 1) is that, in contrast to the previous study, it does not require specialized STEM hardware, makes use of the microscope scripting software provided and can be performed using more widely available electron microscope camera systems. These improvements will make the implementation of this technique more broadly accessible to the electron crystallography community. The STEM serial electron diffraction workflow starts by screening the grid at low magnification with minimal dose delivered to the sample. Using the low-magnification screening, regions of interest (i.e. potential crystals) are identified and selected. Each region of interest is then targeted, and a single electron diffraction pattern is collected. The data are then processed and combined to produce serial electron diffraction datasets for subsequent data processing and structure determination efforts.

As a proof of concept for this new serial electron diffraction workflow, the authors of the study used two zeolite samples. The first sample, zeolite Y, was used to benchmark the new serial diffraction methodology as it could be directly compared with data collected by standard continuous rotation data collection. With both approaches, a high-resolution structure of zeolite Y was determined with similar quality, thereby validating the serial diffraction approach on this model target. The next target chosen for validation was the zeolite ZSM-25, which represented an exciting sample for the new methodology as electron diffraction had not yet been able to produce datasets of sufficient quality for ab initio structure solution. With the new approach in hand, the authors were able to collect serial electron diffraction data from ZSM-25 and ultimately generate a 99.8% complete dataset to 0.90 Å, which facilitated structure solution by direct methods. This demonstration of the first ever ab initio structure solution of ZSM-25 highlights the power of STEM serial electron diffraction data collection, and the more user-friendly implementation promises to allow more users to take advantage of the technique.

Serial electron diffraction data collection using STEM is an important new approach for the field of electron crystallography. It will be exciting to follow future work in this area including the continued streamlining of the STEM workflow, the extension of the technique to an even broader range of STEM and detector combinations, and the structure determination of more novel targets using the methodology. The possibilities of further combining STEM serial electron diffraction with existing continuous rotation methods will also be interesting to developers and users. Overall, STEM serial electron diffraction has the potential to provide highly automated and rapid structures from nanocrystalline materials that may have proven difficult to study using other structural methods.

References

Bücker, R., Hogan-Lamarre, P., Mehrabi, P., Schulz, E. C., Bultema, L. A., Gevorkov, Y., Brehm, W., Yefanov, O., Oberthür, D., Kassier, G. H. & Dwayne Miller, R. J. (2020). Nat. Commun. 11, 996.

Cichocka, M. O., Ångström, J., Wang, B., Zou, X. & Smeets, S. (2018). J. Appl. Cryst. 51, 1652–1661.

Gemmi, M., La Placa, M. G. I., Galanis, A. S., Rauch, E. F. & Nicolopoulos, S. (2015). J. Appl. Cryst. 48, 718–727.

Gemmi, M., Mugnaioli, E., Gorelik, T. E., Kolb, U., Palatinus, L., Boullay, P., Hovmöller, S. & Abrahams, J. P. (2019). ACS Cent. Sci. 5, 1315–1329.

Glaeser, R. M. (2007). Electron Crystallography of Biological Macromolecules. Oxford University Press.

Hogan-Lamarre, P., Luo, Y., Bücker, R., Miller, R. J. D. & Zou, X. (2024). IUCrJ, 11, 62–77.

Nannenga, B. L. & Gonen, T. (2019). Nat. Methods, 16, 369–379.

Nannenga, B. L., Shi, D., Leslie, A. G. & Gonen, T. (2014). Nat. Methods, 11, 927–930.

Zou, X., Hovmöller, S. & Oleynikov, P. (2011). Electron Crystallography: Electron Microscopy and Electron Diffraction. Oxford University Press.

 

This article was originally published in IUCrJ (2024). 11, 7-8. 

One of the features of the culture of science is the exchange of ideas and the sharing of advances. This typically happens by scientists and their students travelling to conferences, collaborating labs or large-scale user facilities to participate and contribute to discussions and experiments. Such travel is a wonderful way to not only expose each other to new work and ideas but also shape and share culture and habits. Most importantly it has been a great way to educate and train the next generation of scientists to the scientific method and to advance ideas, techniques and equipment.

However, this global scientific exchange has been profoundly perturbed by the COVID-19 pandemic. Changes in user travel behaviours, the use of remote access greatly perfected and adopted during the pandemic, combined with the advent of remarkable advances in artificial intelligence (AI), are aligning in such a way that they may possibly change how large-scale scientific facilities operate. This transformation is still ongoing and the new normal is still evolving.

Most scattering facilities report that while the number of submitted user proposals for experiments is slowly recovering toward pre-pandemic levels, the physical presence of users is recovering at a much slower pace while telepresence is increasing. The implications of this pattern are significant for facility operators. Experiments that were once performed by the users themselves are now dependent more so than before on the local beamline staff, thus pressuring the facility’s operational staff levels. And, while one can argue that remote presence in experiments may indeed expand the user base of facilities, the reality is that this emerging user base may be less able to conduct experiments in the future as students are not hands-on trained as rapidly as in the past. This again places increasing pressure on the local staff needed to support physically experiments at the facility.

This shift in user behaviour is taking place while scientific facilities are generating increasing amounts of data and experiments are becoming more demanding. Upgrades to ESRF and LCLS and planned upgrades at APS, ALS and Diamond facilities as well as the many new facilities that are coming online such as ESS, will add even further to the significant deluge of high-quality data. Innovations in the manipulation of the radiation, use of complex sample environments and other add-ons at the beamlines increase the demands on the experimental team to provide real-time support during the experiment as well as substantial support in the processing and analysis of the data post-experiment.

So how do facilities adjust to the reality that while experiments are growing more demanding and generating more data, user physical participation is slow to recover and shifting more to telepresence?

There is no doubt in my mind that the evolving trend at photon and neutron facilities is destined towards a hybrid model — a blend of traditional onsite work with enhanced remote participation in experiments. Already many facilities are investing heavily to enable this mode of user engagement. While the pandemic accelerated the adoption of remote access and AI tools, the value of in-person collaboration and hands-on experimentation remains irreplaceable. The future most likely holds a coexistence, leveraging the strengths of both virtual and physical realms to drive scientific progress. AI will also be part of that future and this will further add to the shift in the mix of skills needed at the beamline to incorporate the AI/data expertise needed when running an experiment, alongside the increasing numbers of beamline scientists who will be needed to mitigate the smaller number of physically participating users.

The future, where a user proposal will be read by an AI chatbot, its content and purpose understood sufficiently to configure a beamline and predict the initial data from the sample examined, is well within reach (Hexemer et al., 2023). An autonomous AI-driven beamline that will perform routine measurements for users is not that far away either.

So where does that leave user facilities, that community watering hole, bringing scientists and ideas together, fostering collaboration and scientific progress?

Here, we need to remember that the science is in the sample. Scientists who made and understand the sample are irreplaceable when conducting experiments and their collaboration with the beamline staff is essential to success. It is no surprise therefore that the scientific community remains deeply committed and engaged in the future of large-scale facilities. A recent personal example was the user meeting of the Advanced Light Source which focused on the capabilities that users will harness with its upcoming upgrade. It is clear that the user community fully recognizes the value brought by user facilities and is fully invested in their success, irrespective of changing trends in user presence.

The brightest future lies in fully embracing AI to harness automation and autonomy at beamlines where it is useful and adds operational value. Deploying AI in harvesting all essence from the PTBs of data collected is unavoidable, given the volumes that we’ll soon face. Remote participation in experiments must be supplemented by hands-on training to provide sufficient experience to users so that the results obtained are put into the right context. Nothing still replaces the value of getting your hands dirty on a beamline and engaging in strong collaborations with the local staff.

The COVID-19 pandemic together with the advent of AI is acting as a catalyst for change within large-scale scientific facilities, reshaping user behaviours and accelerating the role of AI in beamline automation and the analysis of ever-growing volume of data. As we navigate this transformative landscape, we cannot take much of the past for granted. Training of the next generation of users needs to be a community priority. We need to find new ways to support collaboration and user engagement at the beamline so as to perform the most challenging and impactful experiments and drive innovation. Incorporating AI-driven advances to deal with data will be needed to harvest value from experiments and fuel future breakthroughs. To succeed, facilities will need to collaborate widely with the AI community, access infrastructures to manage and process data and work with sponsors to manage the shift in skills needed in staff at the beamline. This is a significant transformation that is best embraced head-on.

Acknowledgements

Ashley White, Andreas Scholl and Simon Kimber are thanked for helpful comments and discussions.

References

Hexemer, A., Pham, V., Köpp, W., White, A. & Rotenberg, E. (2023). ALS User Meeting September 11–15, 2023, poster.

 

This article was originally published in IUCrJ (2024). 11, 1-2. 

Pupils from IES Alfaguara in Loja, Spain, showed the effects of including phosphorescent substances when growing ammonium dihydrogen phosphate crystals.

We are delighted to announce the winners of the 2023 IUCr Crystal Growing Competition.

Schoolchildren were invited to convey their experiences of growing crystals through a video. The entries were judged on their creativity, aesthetic value, description of the working plan and experimental work, clarity of explanation, scientific background and safety by an international panel. The winners, who receive a certificate and medal, are as follows:

Category: 15–18 - GOLD: Ejercito de los Andes, Catriel (Argentina); SILVER: IRIS School, Thai Nguyen City (Vietnam); BRONZE: Escuela No. 751 "Dr Luis Federico Leloir", Trelew (Argentina)

Category: 11–15 - GOLD: Marie Curie Science Club, HNUE, Hanoi (Vietnam); SILVER: IES Alfaguara, Loja (Spain); BRONZE: Global Science Journey, Nguyen Tat Thanh School, Hanoi (Vietnam)

and the winning videos can be watched here.

Many thanks to all those who took part. Once again, the quality of the entries was extremely high, making the decisions difficult for the judges. We are looking forward to receiving more sparkling videos in the 2024 competition!

Photo of Yvonne Mascarenhas from an interview by Fabrício Marques published in Pesquisa FAPESP. Image credit: Léo Ramos Chaves / Revista Pesquisa FAPESP.

Yvonne Mascarenhas, a pioneering Brazilian crystallographer, has won the Fifth Carolina Bori "Science & Woman” Award in the area of ​​Engineering, Exact and Earth Sciences. The award, a tribute from the Brazilian Society for the Advancement of Science (SBPC) and named after the society's first female president, recognises outstanding Brazilian scientists and future Brazilian scientists of notable talent.

The Award ceremony, which was part of the celebrations of the centenary of Carolina Bori's birth, took place on 6 February 2024 in São Paulo and was broadcast on the SBPC YouTube channel. In the video, Professor Mascarenhas, who was nominated for the award by the Brazilian Association of Crystallography and the Brazilian Societies of Physics and Research in Materials, gives a message of encouragement to young women who wish to pursue a scientific career.

When talking about her work as a professor, Mascarenhas told the SBPC how happy she was to have trained a “reasonable” number of masters and doctors who work in Brazil and around the world, and who helped to found and establish a community in this multidisciplinary area. “This is my immediate and most important contribution to society,” she said.

Reflecting on her career, Professor Mascarenhas said she feels privileged. “I didn’t need to break the famous glass ceiling, because my laboratory was the only one – with support from CNPq (National Council for Scientific and Technological Development) and FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo) – of crystallography in Brazil and, as a result, I had to interact with many areas and this interaction opened doors for me. People liked my work and so I progressed in my career very smoothly, without any setbacks and often with the support of other people. Even for administrative positions, such as IFSC Management, I was approached and asked if I would like to take over. But it’s not like that for everyone,” she admitted, recognizing the struggle of many women to pursue a scientific career. “We have had fantastic women. Therefore, I recommend that young women, when making their choices, do so in the best way possible, analyzing their options. And, more than anything, we need to focus, because life, especially for us women, is not peaceful. We cannot give up on starting a family and having children, because this is all part of life. But, if a person wants to have a scientific career, they need to have focus and persistence, even in the face of difficulties”, she concluded.

At the age of 92 Professor Mascarenhas is still active, as a collaborator and retired professor, at the São Carlos Physics Institute (IFSC). See more information about her career here.

 

This article is an abridged version of "Focus and persistence" published in the 5 February 2024 edition of JC Notícias, the electronic edition of SBPC's Jornal da Ciência.

Alexander Wlodawer.

Our colleague and eminent crystallographer Alexander Wlodawer (National Cancer Institute, Frederick, MD, USA) has been awarded the prestigious Casimir Funk Award by the Kosciuszko Foundation (KF) and the Polish Institute of Arts and Sciences of America (PIASA).

For historical record, Tadeusz Kosciuszko ("pure son of liberty" in the words of his friend, Thomas Jefferson) is a national hero in Poland and the United States. Kazimierz (Casimir) Funk is the discoverer of vitamines (later called vitamins).

The award ceremony and lecture took place on 5 April 2024 in the KF headquarters in New York.

For more information about Dr Wlodawer's lecture “What can structural biology do for human health and the environment?” see here.

In early February 2024, the ECA’s Executive Committee had the chance to visit the Poznań Congress Center, where ECM35 is envisioned to be held, and to speak not only with the main local organisers Grygoriy Dmytriv (second from right) and Maciej Kubicki (fourth from right) but also with people from Adam Mickiewicz University (AMU), the Professional Congress Organisation (AMU Foundation) and the Poznań Convention Bureau.

European crystallographers usually hold their Crystallographic Meeting (ECM) in Europe in years when the IUCr does not organise its triennial congress. 2023 was such a year without an ECM when the worldwide crystallographic community, including Europe, gathered in Melbourne, Australia. The ECA Council meeting was held remotely in two different sessions, on 20 and 22 September 2023, to ensure as high an attendance as possible. Meetings of the Executive Committee were organised remotely just before the Council Meeting on 11 and 12 September 2023, and in Poznań, Poland, from 2 to 4 February 2024.

A considerable part of the business at the ECA Council Meetings naturally concerns the planning of future ECMs and European Crystallographic Schools (ECSs), and Councillors receive progress updates on the planning of upcoming meetings as well as the opportunity to vote for the location of future ECMs and ECSs. Due to the ongoing conflict in Ukraine, where ECM35 was due to be held in 2025, a proposal was made by the Polish Crystallographic Association in collaboration with ECM35 organisers and other Ukrainian colleagues, to move the location of the conference to Poznań, which the Council approved. A bid from Czech colleagues to hold ECM36 in Prague (at the same location as the 25th IUCr Congress in 2021) was also presented and approved.

The Prizes awarded by the ECA were also discussed in detail. The oldest Prize is the Max Perutz Prize, which is awarded in recognition of meritorious achievements in any branch of crystallography, and the Erwin Félix Lewy Bertaut Prize, which is awarded jointly with the European Neutron Scattering Association to a young scientist in recognition of notable experimental, methodological or theoretical contributions to the investigation of matter using crystallographic or neutron scattering methods. The Alajos Kálmán Prize was created more recently and is awarded jointly with the Hungarian Chemical Society in recognition of outstanding scientific contributions in the field of structural sciences. At the Council meeting in September, proposals were heard and approved for the incorporation of two new prizes related to the ECA: the George M. Sheldrick Prize (awarded to a non-tenured researcher for outstanding scientific contributions in the field of structural sciences) and the Lodovico Riva de Sanseverino Prize (awarded in recognition of notable contributions to the dissemination of crystallography in the field of education at all levels). More information is available here. All five prizes will be awarded this year at ECM34 in Padova, Italy, in August.

Finally, of interest to younger members of the crystallographic community, the ECA Council heard the report on ECS8, which took place in person in Berlin, Germany, in June 2023. ECSs combine lectures and hands-on tutorials to provide attendees with a fundamental understanding of the principles underpinning crystallography. Each school is unique, however, and offers a variety of topics to cater to a broad range of interests. ECS9 will take place in 2024 in Nancy, France, and a successful bid takes ECS10 to Ohrid, North Macedonia, in 2025. For further information about the ECS, please go here.

The ECA finances were discussed extensively: it was decided to increase the part of the budget allocated to support schools, workshops and congresses, in particular for the ECA’s own ECSs.

The ECA’s General Interest Group 3 (GIG-03: Education in Crystallography) received a grant from the Royal Society of Chemistry (£5k), for the preparation and dissemination of a series of ten video interviews to celebrate the importance of women in the field of crystallography and science, while, at the same time, raising awareness about the pitfalls and obstacles on the way to reach gender parity in science. The first video will be launched in June 2024. GIG-03 is also preparing a Crystallography Video Competition (using a grant obtained from the Cambridge Crystallographic Data Centre) for schools in different European countries. The aim is to present the winning videos at ECM34.

European crystallographers continue to meet during the ECA’s monthly lunchtime webinars, a successful series of online seminars covering all sub-disciplines in crystallography and organised by the ECA’s young crystallographers. Eight lunchtime webinars took place from April 2023 until March 2024. Another series of webinars, the Distinguished Lectures on Quantum Crystallography and Complementary Fields, initiated by the ECA’s Special Interest Group 2 (SIG-02: Quantum Crystallography) and the IUCr Commission on Quantum Crystallography continued as well: eight webinars took place, usually with two lectures during the same webinar.