Author

Christian Holm

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#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

#machine learning Sep 2025

Deep Learning-Driven Peptide Classification in Biological Nanopores

This work translates the peptide identification problem into an image-classification task by transforming each resistive pulse into a scaleogram via the continuous wavelet transform, a representation that jointly encodes amplitude, frequency, and time in a form well suited for deep convolutional models.

S. Tovey, Julian Hoßbach, Sandro Kuppel et al. · arXiv.org · 1 citation