Author

Yu. Lysogorskiy

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Preprint Sep 2026

A latent-space extrapolation grade built into graph atomic cluster expansion foundation potentials

Foundation machine-learning interatomic potentials cover broad configurational and chemical spaces, but their reliability can vary across the atomic environments encountered during a simulation. Here we introduce the calibrated Mahalanobis (CALM) extrapolation grade $\gamma$, a piecewise differentiable per-atom quantit...

Yu. Lysogorskiy, A. Bochkarev, R. Drautz · 0 citations

#machine learning Preprint Sep 2026

A strategic roadmap for an atomistic machine-learning ecosystem

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 widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging...

Jörg Behler, Michele Ceriotti, Cecilia Clementi et al. · 0 citations

Preprint Jul 2026

Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Aromatic organic solutes in water exhibit a delicate balance between hydrophobic solvation and directional O-H$\cdots \pi$ hydrogen bonds, yet widely used force fields and state-of-the-art density functional approaches struggle to provide a consistent picture of these pivotal interactions. We introduce a data-efficient...

N. Stolte, H. Forbert, Yu. Lysogorskiy et al. · 0 citations