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Author

S. Boresch

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#graph neural networks Open access Sep 2026

GRACE-OFF: A Machine-Learned Interatomic Potential for Organic Liquids Using the GRACE Architecture

benchmarks demonstrate that GRACE-OFF achieves higher MD performance than comparable MACE-OFF models in both single and double precision, establishing GRACE-OFF as an accurate and computationally efficient foundation potential for routine simulations of organic liquids and biomolecular systems.

Anna Katharina Picha, Johannes Karwounopoulos, Linus C. Erhard et al. · 2 citations
Open access Jul 2026

Dual-Coordinate Relative Free Energy Simulations Using Machine-Learned Interatomic Potentials

This work employs a dual-coordinate approach where both solutes are explicitly present but do not interact with one another, and utilizes a harmonic ″anchor″ restraint to a central atom on each molecule to enforce spatial overlap without modifying the internal intramolecular dynamics of either solute.

Anna Katharina Picha, S. Boresch · 1 citation

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