Publications
See Volker's Google Scholar profile for a full list and up-to-date metrics.
Key publications: ML interatomic potentials
Synthetic pre-training for neural-network interatomic potentials
Indirect learning and physically guided validation of interatomic potential models
Key publications: Amorphous structures
Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics
Geometrically frustrated interactions drive structural complexity in amorphous calcium carbonate
Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure
Key publications: Functional materials by design
Accelerated First-Principles Exploration of Structure and Reactivity in Graphene Oxide
Device-scale atomistic modelling of phase-change memory materials
Coarse-grained versus fully atomistic machine learning for zeolitic imidazolate frameworks
Review, perspective, and commentary articles
- The amorphous state as a frontier in computational materials design (Nature Reviews Materials, 2025)
- Data as the next challenge in atomistic machine learning (Nature Computational Science, 2024)
- How to validate machine-learned interatomic potentials (The Journal of Chemical Physics, 2023)
- Simulations in the era of exascale computing (Nature Reviews Materials, 2023)
- Gaussian Process Regression for Materials and Molecules (Chemical Reviews, 2021)