249 citations · 289 across the 15 of their papers we have counts for
17 papers
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
Multi-modal transformer for signal classification in nanopore blockade experiments
Sandro Kuppel, Julian Hoßbach, Samuel Tovey +1
Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ionic current as analytes enter…
Reinforcement Learning Enables Autonomous Microrobot Navigation and Intervention in Simulated Blood Capillaries
Jannik Drotleff, Samuel Tovey, Paul Hohenberger +4
Autonomous microrobots navigating biological vasculature could enable targeted drug delivery and thrombolysis, yet training control policies for realistic environments remains an o…
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
Konstantin Nikolaou, Jonas Scheunemann, Sven Krippendorf +2
Neural scaling laws describe predictable power-law relationships between model size, dataset size, compute, and performance. While these laws guide the development of modern founda…
Deep Learning-Driven Peptide Classification in Biological Nanopores
Julian Hoßbach, Samuel Tovey, Sandro Kuppel +3
Nanopore-based single-molecule sensing is a promising route to fast, low-cost disease diagnosis and protein sequencing: as an analyte such as a peptide or protein traverses a nanos…
Beyond Scaling Curves: Internal Dynamics of Neural Networks Through the NTK Lens
Konstantin Nikolaou, Sven Krippendorf, Samuel Tovey +1
Scaling laws offer valuable insights into the relationship between neural network performance and computational cost, yet their underlying mechanisms remain poorly understood. In t…
Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Moritz René Schäfer, Nico Segreto, Fabian Zills +2
We introduce Atomistic learned potentials in JAX (apax), a flexible and efficient open source software package for training and inference of machine-learned interatomic potentials.…
Quantum vs. classical: A comprehensive benchmark study for predicting time series with variational quantum machine learning
Tobias Fellner, David Kreplin, Samuel Tovey +1
Variational quantum machine learning algorithms have been proposed as promising tools for time series prediction, with the potential to handle complex sequential data more effectiv…
Collective variables of neural networks: empirical time evolution and scaling laws
Samuel Tovey, Sven Krippendorf, Michael Spannowsky +2
This work presents a novel means for understanding learning dynamics and scaling relations in neural networks. We show that certain measures on the spectrum of the empirical neural…
Peptide Classification from Statistical Analysis of Nanopore Translocation Experiments
Julian Hoßbach, Samuel Tovey, Tobias Ensslen +2
Protein characterization using nanopore-based devices promises to be a breakthrough method in basic research, diagnostics, and analytics. Current research includes the use of machi…
SwarmRL: Building the Future of Smart Active Systems
Samuel Tovey, Christoph Lohrmann, Tobias Merkt +6
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 microscopi…
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning
Samuel Tovey, Christoph Lohrmann, Christian Holm
Reinforcement learning (RL) is a flexible and efficient method for programming micro-robots in complex environments. Here we investigate whether reinforcement learning can provide…
Generating Quantum Reservoir State Representations with Random Matrices
Samuel Tovey, Tobias Fellner, Christian Holm +1
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 computationa…
Emergence of Accurate Atomic Energies from Machine Learned Noble Gas Potentials
Frank Uhlig, Samuel Tovey, Christian Holm
The quantum theory of atoms in molecules (QTAIM) gives access to well-defined local atomic energies. Due to their locality, these energies are potentially interesting in fitting at…
ZnTrack -- Data as Code
Fabian Zills, Moritz Schäfer, Samuel Tovey +2
The past decade has seen tremendous breakthroughs in computation and there is no indication that this will slow any time soon. Machine learning, large-scale computing resources, an…
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang +85
Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much…
Robustness of quantum algorithms against coherent control errors
Julian Berberich, Daniel Fink, Christian Holm
Coherent control errors, for which ideal Hamiltonians are perturbed by unknown multiplicative noise terms, are a major obstacle for reliable quantum computing. In this paper, we pr…