Exploration of transferable and uniformly accurate neural network interatomic potentials using optimal experimental design. - Viktor Zaverkin; Johannes Kästner (Mach. Learn. Sci. Technol., 2021)

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. - Viktor Zaverkin; David Holzmüller; Ingo Steinwart; Johannes Kästner (DaRUS, 2021)

Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreaching. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Takashi Maruyama; Mathias Niepert; Francesco Alesiani (CoRR, 2023)

A Framework and Benchmark for Deep Batch Active Learning for Regression. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (J. Mach. Learn. Res., 2023)

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (DaRUS, 2023)

Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. - Duy Minh Ho Nguyen; Nina Lukashina; Tai Nguyen 008; An T. Le 001; TrungTin Nguyen; Nhat Ho; Jan Peters 001; Daniel Sonntag; Viktor Zaverkin; Mathias Niepert (ICML, 2024)

Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing. - Viktor Zaverkin; Francesco Alesiani; Takashi Maruyama; Federico Errica; Henrik Christiansen; Makoto Takamoto; Nicolas Weber; Mathias Niepert (NeurIPS, 2024)

Physics-Informed Weakly Supervised Learning for Interatomic Potentials. - Makoto Takamoto; Viktor Zaverkin; Mathias Niepert (CoRR, 2024)

Adaptive Width Neural Networks. - Federico Errica; Henrik Christiansen; Viktor Zaverkin; Mathias Niepert; Francesco Alesiani (CoRR, 2025)

Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks. - Maya Bechler-Speicher; Ben Finkelshtein; Fabrizio Frasca; Luis Müller; Jan Tönshoff; Antoine Siraudin; Viktor Zaverkin; Michael M. Bronstein; Mathias Niepert; Bryan Perozzi; Mikhail Galkin 001; Christopher Morris 001 (CoRR, 2025)

Geometric Kolmogorov-Arnold Superposition Theorem. - Francesco Alesiani; Takashi Maruyama; Henrik Christiansen; Viktor Zaverkin (CoRR, 2025)

Fast, Modular, and Differentiable Framework for Machine Learning-Enhanced Molecular Simulations. - Henrik Christiansen; Takashi Maruyama; Federico Errica; Viktor Zaverkin; Makoto Takamoto; Francesco Alesiani (CoRR, 2025)