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"Beyer"}, {"first" : "Christian", "last" : "Holm"} ], "volume": "34","number": "4", "issn" : "1521-3919", "doi" : "10.1002/mats.202500020", "bibtexKey": "Burth_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/27c39b38c8ac8d3f348faa523a9f5b1b3/bbenjaminspeck", "tags" : [ "icp","peerreviewed","postprint" ], "intraHash" : "7c39b38c8ac8d3f348faa523a9f5b1b3", "interHash" : "dca44640e11d9dd04aa19b0885241d9d", "label" : "Zero shot molecular generation via similarity kernels", "user" : "bbenjaminspeck", "description" : "", "date" : "2025-07-10 10:14:55", "changeDate" : "2025-07-10 10:14:55", "count" : 5, "pub-type": "article", "journal": "Nature Communications", "year": "2025", "url": "https://doi.org/10.1038/s41467-025-60963-3", "author": [ "Rokas Elijosius","Fabian Zills","Ilyes Batatia","Sam Walton Norwood","Dávid Péter Kovács","Christian Holm","Gábor Csányi" ], "authors": [ {"first" : "Rokas", "last" : "Elijosius"}, {"first" : "Fabian", "last" : "Zills"}, {"first" : "Ilyes", "last" : "Batatia"}, {"first" : "Sam Walton", "last" : "Norwood"}, {"first" : "Dávid Péter", "last" : "Kovács"}, {"first" : "Christian", "last" : "Holm"}, {"first" : "Gábor", "last" : "Csányi"} ], "volume": "16","number": "1","pages": "5991","abstract": "Generative modelling aims to accelerate the discovery of novel chemicals by directly proposing structures with desirable properties. Recently, score-based, or diffusion, generative models have significantly outperformed previous approaches. Key to their success is the close relationship between the score and physical force, allowing the use of powerful equivariant neural networks. However, the behaviour of the learnt score is not yet well understood. Here, we analyse the score by training an energy-based diffusion model for molecular generation. We find that during the generation the score resembles a restorative potential initially and a quantum-mechanical force at the end, exhibiting special properties in between that enable the building of large molecules. Building upon these insights, we present Similarity-based Molecular Generation (SiMGen), a new zero-shot molecular generation method. SiMGen combines a time-dependent similarity kernel with local many-body descriptors to generate molecules without any further training. Our approach allows shape control via point cloud priors. Importantly, it can also act as guidance for existing trained models, enabling fragment-biased generation. We also release an interactive web tool, ZnDraw, for online SiMGen generation (https://zndraw.icp.uni-stuttgart.de).", "issn" : "2041-1723", "doi" : "10.1038/s41467-025-60963-3", "bibtexKey": "Elijošius2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2144040b6b4a14e95b80e9fd3b8a97fa8/bbenjaminspeck", "tags" : [ "sfb1313","icp","dfg","peerreviewed","postprint" ], "intraHash" : "144040b6b4a14e95b80e9fd3b8a97fa8", "interHash" : "6540701198ee27bafd44ab861fa554d3", "label" : "Intermittent flow paths in biofilms grown in a microfluidic channel", "user" : "bbenjaminspeck", "description" : "", "date" : "2025-06-27 15:18:47", "changeDate" : "2025-06-27 15:18:47", "count" : 7, "pub-type": "article", "journal": "Advances in Water Resources","publisher":"Elsevier BV", "year": "2025", "url": "http://dx.doi.org/10.1016/j.advwatres.2025.105018", "author": [ "Kerem Bozkurt","Christoph Lohrmann","Felix Weinhardt","Daniel Hanke","Raphael Hopp","Robin Gerlach","Christian Holm","Holger Class" ], "authors": [ {"first" : "Kerem", "last" : "Bozkurt"}, {"first" : "Christoph", "last" : "Lohrmann"}, {"first" : "Felix", "last" : "Weinhardt"}, {"first" : "Daniel", "last" : "Hanke"}, {"first" : "Raphael", "last" : "Hopp"}, {"first" : "Robin", "last" : "Gerlach"}, {"first" : "Christian", "last" : "Holm"}, {"first" : "Holger", "last" : "Class"} ], "pages": "105018", "issn" : "0309-1708", "doi" : "10.1016/j.advwatres.2025.105018", "bibtexKey": "Bozkurt_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2e80987877f581d5b2c4ad689d580d77b/bbenjaminspeck", "tags" : [ "icp","peerreviewed","postprint" ], "intraHash" : "e80987877f581d5b2c4ad689d580d77b", "interHash" : "e7bca106c195d4464b1305ebaadfd1ff", "label" : "Emergence of accurate atomic energies from machine-learned noble-gas potentials", "user" : "bbenjaminspeck", "description" : "", "date" : "2025-05-13 14:11:00", "changeDate" : "2025-05-13 14:11:00", "count" : 6, "pub-type": "article", "journal": "The Journal of Chemical Physics","publisher":"AIP Publishing", "year": "2025", "url": "http://dx.doi.org/10.1063/5.0227640", "author": [ "Frank Uhlig","Samuel Tovey","Christian Holm" ], "authors": [ {"first" : "Frank", "last" : "Uhlig"}, {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christian", "last" : "Holm"} ], "volume": "162","number": "18", "issn" : "1089-7690", "doi" : "10.1063/5.0227640", "bibtexKey": "Uhlig_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2092846c63dd93a995bc518498e3282ce/bbenjaminspeck", "tags" : [ "icp","peerreviewed","postprint" ], "intraHash" : "092846c63dd93a995bc518498e3282ce", "interHash" : "ba34e41c75ec5ddd320cf4594272c194", "label" : "Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning", "user" : "bbenjaminspeck", "description" : "", "date" : "2025-05-01 17:21:54", "changeDate" : "2026-01-16 07:59:03", "count" : 5, "pub-type": "article", "journal": "Machine Learning: Science and Technology", "year": "2026", "url": "https://doi.org/10.1088/2632-2153/ae365f", "author": [ "Tobias Fellner","David Kreplin","Samuel Tovey","Christian Holm" ], "authors": [ {"first" : "Tobias", "last" : "Fellner"}, {"first" : "David", "last" : "Kreplin"}, {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christian", "last" : "Holm"} ], "abstract": "Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectively than classical approaches. However, their practical advantage over established classical methods remains uncertain. In this work, we present a comprehensive benchmark study comparing a range of variational quantum algorithms and classical machine learning models for time series forecasting. We evaluate their predictive performance on three chaotic systems across 27 time series prediction tasks of varying complexity, and ensure a fair comparison through extensive hyperparameter optimization. Our results indicate that, in many cases, quantum models struggle to match the accuracy of simple classical counterparts of comparable complexity. Furthermore, we analyze the predictive performance relative to the model complexity and discuss the practical limitations of variational quantum algorithms for time series forecasting.", "eprint" : "2504.12416", "archiveprefix" : "arXiv", "primaryclass" : "quant-ph", "doi" : "10.1088/2632-2153/ae365f", "bibtexKey": "Fellner_2026" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2925b63c636fac32711b8933dd38dc992/bbenjaminspeck", "tags" : [ "icp","peerReviewed","postprint" ], "intraHash" : "925b63c636fac32711b8933dd38dc992", "interHash" : "bc7a090cbd1692c4438fb1083794df8c", "label" : "SwarmRL: building the future of smart active systems", "user" : "bbenjaminspeck", "description" : "SwarmRL: building the future of smart active systems | The European Physical Journal E", "date" : "2025-04-15 12:12:54", "changeDate" : "2025-04-15 12:12:54", "count" : 5, "pub-type": "article", "journal": "The European Physical Journal E", "year": "2025", "url": "https://doi.org/10.1140/epje/s10189-025-00477-4", "author": [ "Samuel Tovey","Christoph Lohrmann","Tobias Merkt","David Zimmer","Konstantin Nikolaou","Simon Koppenhöfer","Anna Bushmakina","Jonas Scheunemann","Christian Holm" ], "authors": [ {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Christoph", "last" : "Lohrmann"}, {"first" : "Tobias", "last" : "Merkt"}, {"first" : "David", "last" : "Zimmer"}, {"first" : "Konstantin", "last" : "Nikolaou"}, {"first" : "Simon", "last" : "Koppenhöfer"}, {"first" : "Anna", "last" : "Bushmakina"}, {"first" : "Jonas", "last" : "Scheunemann"}, {"first" : "Christian", "last" : "Holm"} ], "volume": "48","number": "4","pages": "16","abstract": "This work introduces SwarmRL, a Python package designed to study intelligent active particles. SwarmRL provides an easy-to-use interface for developing models to control microscopic colloids using classical control and deep reinforcement learning approaches. These models may be deployed in simulations or real-world environments under a common framework. We explain the structure of the software and its key features and demonstrate how it can be used to accelerate research. With SwarmRL, we aim to streamline research into micro-robotic control while bridging the gap between experimental and simulation-driven sciences. SwarmRL is available open-source on GitHub at https://github.com/SwarmRL/SwarmRL.", "issn" : "1292-895X", "doi" : "10.1140/epje/s10189-025-00477-4", "bibtexKey": "Tovey2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/2c82c0ee76304097955291da87e997d76/bbenjaminspeck", "tags" : [ "icp","peerreviewed","postprint" ], "intraHash" : "c82c0ee76304097955291da87e997d76", "interHash" : "65f82cf22c6ca53ef1d05dddeaaec8b7", "label" : "Generating quantum reservoir state representations with random matrices", "user" : "bbenjaminspeck", "description" : "Generating quantum reservoir state representations with random matrices - IOPscience", "date" : "2025-04-15 11:58:20", "changeDate" : "2025-04-15 11:58:20", "count" : 8, "pub-type": "article", "journal": "Machine Learning: Science and Technology","publisher":"IOP Publishing", "year": "2025", "url": "https://dx.doi.org/10.1088/2632-2153/adc0e2", "author": [ "Samuel Tovey","Tobias Fellner","Christian Holm","Michael Spannowsky" ], "authors": [ {"first" : "Samuel", "last" : "Tovey"}, {"first" : "Tobias", "last" : "Fellner"}, {"first" : "Christian", "last" : "Holm"}, {"first" : "Michael", "last" : "Spannowsky"} ], "volume": "6","number": "1","pages": "015068","abstract": "We demonstrate a novel approach to reservoir computation measurements using random matrices. We do so to motivate how atomic-scale devices could be used for real-world computational applications. Our approach uses random matrices to construct reservoir measurements, introducing a simple, scalable means of generating state representations. In our studies, two reservoirs, a five-atom Heisenberg spin chain and a five-qubit quantum circuit, perform time series prediction and data interpolation. The performance of the measurement technique and current limitations are discussed in detail, along with an exploration of the diversity of measurements provided by the random matrices. In addition, we explore the role of reservoir parameters such as coupling strength and measurement dimension, providing insight into how these learning machines could be automatically tuned for different problems. This research highlights the use of random matrices to measure simple quantum reservoirs for natural learning devices, and outlines a path forward for improving their performance and experimental realization.", "doi" : "10.1088/2632-2153/adc0e2", "bibtexKey": "Tovey_2025" } , { "type" : "Publication", "id" : "https://puma.ub.uni-stuttgart.de/bibtex/29ae39615691f4d6dd30f784ed70a4a0d/bbenjaminspeck", "tags" : [ "postPrint","icp","peerReviewed" ], "intraHash" : "9ae39615691f4d6dd30f784ed70a4a0d", "interHash" : "2b4c1b1fa9fbd11984606e1a4692e062", "label" : "CO2-induced Drastic Decharging of Dielectric Surfaces in Aqueous Suspensions.", "user" : "bbenjaminspeck", "description" : "2401.00096", "date" : "2025-02-11 11:09:21", "changeDate" : "2025-02-11 11:09:21", "count" : 5, "pub-type": "misc", "year": "2024", "url": "https://arxiv.org/abs/2401.00096", "author": [ "Peter Vogel","David Bayer","Christian Holm","Thomas Palberg" ], "authors": [ {"first" : "Peter", "last" : "Vogel"}, {"first" : "David", "last" : "Bayer"}, {"first" : "Christian", "last" : "Holm"}, {"first" : "Thomas", "last" : "Palberg"} ], "eprint" : "2401.00096", "archiveprefix" : "arXiv", "primaryclass" : "physics.chem-ph", "bibtexKey": "batatia2024foundationmodelatomisticmaterials" } ] }