7th MACSMIN 2026: Mathematics and Computer Science for Materials Innovation
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The MACSMIN logo includes the basic examples of the rock-salt cubic crystal, the benzene ring, and a blue wave containing a local maximum and a local minimum. |
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The CCP-QC (UK Collaborative Computational Project in Quantum Computing) has generously funded travel and accommodation for participants of the 7th MACSMIN. |
- Dates: 26-29 May 2026 in a hybrid form in the Materials Innovation Factory, Liverpool, UK.
- Organisers: Data Science group including Vitaliy Kurlin, Olga Anosova, and Dan Widdowson.
- The registration is free: e-mail Vitaliy Kurlin, see the the travel information for Liverpool (UK).
- Invited speakers (in alphabetical order)
- Simon Billinge (Materials Science, University of California Santa Barbara, US)
- Pavel Buividovich (Mathematical Sciences, University of Liverpool, UK)
- Richard Catlow FRS (Materials Science, University College London, UK)
- Daniel Colquitt (Mathematical Sciences, University of Liverpool, UK)
- Alexandre de Brevern (Bioinformatics, Université Paris Cité, France)
- John R. Helliwell (Protein Crystallography, University of Manchester, UK)
- Wolfgang Hornfeck (Institute of Physics, Czech Academy of Sciences)
- Alexei Lisitsa (Computer Science, University of Liverpool, UK)
- Thérèse Malliavin (Structural Bioinformatics, University of Lorraine, France)
- Greg McColm (Crystallography, University of South Florida, US)
- Berthold Stoeger (Crystallography, TU Wien, Austria)
- Alessandro Troisi (Chemistry, University of Liverpool, UK)
- Alex Wlodawer (Structural Biology, National Cancer Institute, US)
- Talks on advances in Geometric Data Science will be given by Daniel Widdowson, Olga Anosova, Yury Elkin, Ziqiu Jiang, Surya Majumder, Jack Gallimore, and Vitaliy Kurlin.
- If you would like to give a talk in person, please e-mail your title and abstract (max 300 words) to Vitaliy Kurlin before 20th April 2026. We might have a few slots for short 20-min talks.
- All talks will be aimed for a broad audience of scientists including PhD students and postdocs.
- Related meetings: mini-symposium Computational Data Science of Nanostructures (6 hours) at the SIAM annual meeting on July 28 - August 1, 2025 in Montreal: part 1, part 2, part 3.
- The AMS session on Open Problems in Geometric Data Science at JMM 2026, Washington DC.
- The ICERM workshop Rigidity Theory meets Geometric Data Science for applications in chemistry and biology on 6-10 July 2026, Brown University, Providence (US).
- The regular MIF++ seminar is a continuous version of the annual MACSMIN.
- Past meetings of the MACSMIN series: 2025, 2024, 2023, 2022, 2021, 2020.
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Timetable (provisional, all UK times) Tuesday Wednesday Thursday Friday
Tuesday 26 May (the MIF boardroom)
- 08.45-09.00 Opening, history, and vision of MACSMIN by Vitaliy Kurlin (Liverpool MIF, UK)
- 09.00-09.45 TBA
- 10.00-10.45 TBA
- 11.00-11.45 TBA
- 12.00-13.30 lunch (covered by the organisers)
- 13.30-14.15 TBA
- 14.30-15.15 TBA
- 16.00-17.00 Vitaliy Kurlin (Materials Innovation Factory, Liverpool, UK)
- Title. Geometric Data Science (inaugural lecture organised by the CSI school).
- Abstract. The emerging area of Geometric Data Science studies real data objects under practical equivalences [1]. The key example is a point cloud under isometry or rigid motion. In a Euclidean space, if given points are ordered, their isometry class is uniquely determined by the matrix of pairwise distances. A naive extension to m unordered points requires exponentially many matrices depending on m! permutations. The case of m=3 points was settled 2000+ years ago due to the side-side-side theorem from school geometry. However, even for m=4 points in the plane, there are infinitely many 4-point clouds that are indistinguishable by 6 pairwise distances. The talk will describe a simple invariant that finished the case of 4 points [2] and a complete invariant with Lipschitz continuous metrics that resolved the exponential challenge in polynomial time in the number of points for any fixed Euclidean dimension [3].
- [1] O.Anosova, V.Kurlin. Geometric Data Science book (arxiv.org:2512.05040), the latest version at https://kurlin.org/Geometric-Data-Science-book.pdf.
- [2] D.Widdowson, V.Kurlin. Resolving the data ambiguity for periodic crystals. NeurIPS 2022, v.35, p.24625-24638. Extended version to appear in SIAM J Applied Mathematics.
- [3] D.Widdowson, V.Kurlin. Recognizing rigid patterns of unlabeled point clouds by complete and continuous isometry invariants with no false negatives and no false positives. CVPR 2023, p.1275-1284. Extended at arxiv:2303.14161, arxiv:2303.13486.
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Wednesday 27 May (the MIF boardroom)
- 09.00-09.45 John R. Helliwell (University of Manchester, UK)
- Title. Precision and Accuracy in Biological Crystallography, Diffraction, Scattering, Microscopies, and Spectroscopies.
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Thursday 28 May (CMD Seminar room)
- 16.00-16.45 Simon Billinge (Materials Science, University of California Santa Barbara, US)
- Title. TBA
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Friday 29 May (the MIF boardroom)
- 09.00-09.45 Alex Wlodawer (Structural Biology, National Cancer Institute, US)
- Title. Community efforts to improve the contents of the Protein Data Bank, a crucial resource for structural biology.
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Abstracts of talks (in alphabetical order)
- Alexandre de Brevern (Bioinformatics, Université Paris Cité, France)
- Title. Protein Blocks as a discrete geometric model for protein conformational spaces.
- Abstract. Protein structures are commonly described by secondary-structure elements such as helices, strands, and loops, but this coarse representation is often insufficient to capture fine local deformations, transient conformers, and disorder-related rearrangements. Structural alphabets provide a more systematic discretization of protein geometry. Among them, the Protein Block (PB) alphabet, composed of 16 local backbone prototypes, offers a compact and precise encoding of three-dimensional protein conformations.
We present a PB-based framework for representing proteins and molecular dynamics trajectories as symbolic paths in a discrete conformational space. By encoding overlapping backbone fragments into PB sequences, static structures and trajectory ensembles can be transformed into objects that are both geometrically meaningful and computationally tractable. This discrete formalism enables residue-level analysis of local state occupancy, transition frequencies, conformational heterogeneity, persistence, and deformability. It also supports direct comparison of proteins, structural ensembles, and dynamical behaviours using sequence-like or graph-based methods.
Such a representation is particularly useful for identifying flexible hinges, transient local states, weakly structured segments, and disorder-prone regions that are often difficult to characterize using global descriptors such as RMSD or averaged coordinates alone. More generally, PBs provide an effective bridge between continuous protein geometry and discrete algorithmic analysis, allowing the study of conformational landscapes in terms of symbolic dynamics, local transitions, and geometric state organization.
This framework is well suited to the exploration of protein conformational spaces, the analysis of flexibility and disorder, and the characterization of disease-associated local structural changes. It therefore offers a mathematically manageable representation for linking geometry, dynamics, and function in computational structural biology.
- John R. Helliwell (University of Manchester, UK)
- Title. Precision and Accuracy in Biological Crystallography, Diffraction, Scattering, Microscopies, and Spectroscopies.
- Abstract. The talk is based on the recent book, which provides the first comprehensive treatment of uncertainties in the structures determined by all relevant experimental methods; includes an improved set of database resources, governed by data deposition policies set by experts in the field; and highlights the need for new and improved methods which in turn will lead to important developments in research and equipment manufacturing.
- Wolfgang Hornfeck (Institute of Physics, Czech Academy of Sciences)
- Title. Arithmetic and algebraic patterns in crystal structures.
- Abstract. The classification of crystal structures is traditionally based on the mathematical concept of groups (space, plane, point, etc.). While group theory in general provides a most powerful framework of describing the symmetry of periodically long-range ordered atomic arrangements in three dimensions, it is also well known that it does not encompass all kind of symmetries which might be present. For instance, it cannot account for the full point group symmetry of molecules exhibiting a non-crystallographic rotational symmetry incompatible with lattice translations. Yet, there are still more, and more subtle, patterns hidden in the atomic arrangements, and their coordinate representations, which might be described by other mathematical concepts from the fields of arithmetic, algebra, and geometry, respectively. In my talk I will give a number of examples: (i) similar sublattice structures described by multiplicative congruential generators; (ii) two- and three-dimensional permutation structures; (iii) Z module twin quasilattices. These examples for what one might call quantitative crystal structure descriptors will be discussed in relation to actual crystal structures of intermetallic compounds. Moreover, my talk will address general questions of randomness and order, spatial uniformity, as well as structural complexity in the algorithmic sense of Kolmogorov.
Hornfeck, W. (2012). Acta Cryst. A 68, 167-180; Hornfeck, W. (2013). Acta Cryst. A 69, 355-364; Hornfeck, W. (2025). Struct. Chem. 36, 2007-2019.
- Alexei Lisitsa (Computer Science, University of Liverpool, UK)
- Title. Automated Reasoning for Knots and Knotted Structures.
- Abstract. In this talk, I will present applications of automated reasoning to problems in knot theory and related knotted structures. Fundamental algorithmic tasks—such as unknot detection (i.e., deciding whether a given knot is trivial) and knot equivalence - can be approached by translating these problems into algebraic invariants, including variants of quandles.These translations enable the use of automated reasoning techniques, such as first-order theorem proving, countermodel finding, and constraint-based methods including SAT solving. I will outline how these approaches provide effective tools for analysing knot properties and, in some cases, for certifying non-equivalence. Potential applications include knot detection and analysis in biological and chemical contexts, such as proteins and polymers, where knotted structures naturally arise.
- Thérèse Malliavin (University of Lorraine, France)
- Title. Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations.
- Abstract. Prediction of conformations for intrinsically disordered proteins is mostly based on local structure information, at the contrary of folded protein structure prediction, in which the use of local conformational information is coupled with long- range distance restraints obtained from sequence alignments. The interval Branch-and-Prune algorithm permits to calculate protein conformations using only local conformations knowledge. In that case, the efficiency of calculation is directly related to the knowledge of stereochemistry (bond angles and ω dihedral angles) along the protein sequence and is particularly sensitive to the variations of the dihedral angles ω (da Rocha et al, 2024). The impact of stereochemistry variations is particularly strong in the case of protein topologies defined from numerous long-range restraints, as in the case of protein of β secondary structures. Machine learning methods for classifying ω values would thus be very useful in the frame of conformation prediction for disordered proteins. The prediction of dihedral angles φ and ψ from their sequences has been widely studied in the literature, but the prediction of ω angles has been less explored because it presents more difficulties (Keck et al, 2025). In particular, these angles vary in narrower intervals than the other dihedral angles of the backbone. In addition, random variations of ω angles are produced by the fit of the protein conformations to the X-ray electronic density due to the noise present in the density. Using the interval Branch-and-Prune, we intend to induce a correlation between backbone angles, to improve the efficiency of prediction by Support Vector Machine (SVM), while minimizing the backbone drift induced by modify the angle values. The effects of angle modifications on the prediction of ω angles within sets of homologous proteins and within CATH families of structures, will be presented.
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References:
(da Rocha et al, 2024) da Rocha W, Liberti L, Mucherino A, Malliavin TE. Influence of Stereochemistry in a Local Approach for Calculating Protein Conformations. J Chem Inf Model. 2024 Dec 9;64(23):8999-9008. doi: 10.1021/acs.jcim.4c01232.
(Keck et al, 2025) Keck U, Guermeur Y, Malliavin TE. Effectiveness of Support Vector Machines for the prediction of dihedral angles ω in proteins. Nancy Computational Structural Biology (NCSB) meeting, 19 June 2025, Nancy.
- Alex Wlodawer (Structural Biology, National Cancer Institute, US)
- Title. Community efforts to improve the contents of the Protein Data Bank, a crucial resource for structural biology.
- Abstract. For over half a century, the Protein Data Bank (PDB) has been accumulating the experimental structures of biological macromolecules, becoming a crucial resource created by structural biologists for the use of the whole biological and biomedical community and beyond. With over quarter of a million experimentally determined structure models, it has also served to provide training data for the remarkably successful artificial intelligence-based prediction of protein structures. Implementation by the PDB curators of validation tools has led throughout the years to vast improvement in the quality of PDB deposits, but occasional inaccuracies, mistakes, and errors of different severity are still able to sneak into the database despite tight scrutiny. Several such problems were identified during campaigns that analyzed the quality of structure models of selected protein families, such as β-lactamases, SARS-CoV-2 proteins, cAMP-dependent protein kinases, or L-asparaginases. In many cases, the problems could be attributed to depositors' negligence, lack of training or supervision, or cutting corners by ignoring the warnings in the PDB Validation Reports. Postulates directed to the PDB included elimination of exact duplicates, development of better mechanisms for linking series of model updates, finding a better home for group depositions resulting from fragment screening campaigns, and prominent display of CAVEAT records to warn human and machine users of potential problems in the PDB deposits. Other identified general problems included the lack of modeled solvent in many high-resolution protein crystal structures. Such problem-tracking efforts ultimately aim to alert the PDB and, if possible, suggest solutions to make this crucial resource even better.
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Travel information: venue, accommodation, trains, and flights
- All talks in person will be in the ground floor boardroom in the Materials Innovation Factory (MIF), Liverpool, UK. Address: 51 Oxford street, building 807 in the grid cell F5 on the campus map. The building has a secure entrance, so we will let the reception know about MACSMIN participants. The MIF is 15 min on foot from the Liverpool Lime Street station.
- If you contact us in advance, we can help with booking hotels. One option is the Liner hotel in a quiet street close to the Liverpool Lime Street main rail station. Explore other good hotels and attractions on the website visit Liverpool.
- The city has the Liverpool John Lennon airport with convenient buses to the centre. The larger Manchester airport has the train station with direct 90-min trains to the Liverpool Lime Street station. Check flights to nearby airports at Skyscanner.
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