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20242026
most citedHydrogen liquid-liquid transition from first principles and machine learning

5 citations · 7 across the 5 of their papers we have counts for

collaborators

5 papers

cs.MM2026

Can Agents Win the Video Browser Showdown?

Bastian Jäckl, Zuzana Vopálková, Daniel A. Keim +1

Searching large video collections is typically an interactive process in which users play two roles. First, they hold the search intent: the underlying goal that determines what co…

cs.MM2025★ 1 cited

Dynamic Sub-region Search in Homogeneous Collections Using CLIP

Bastian Jäckl, Vojtěch Kloda, Daniel A. Keim +1

Querying with text-image-based search engines in highly homogeneous domain-specific image collections is challenging for users, as they often struggle to provide descriptive text q…

cs.MM2025★ 1 cited

Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP

Bastian Jäckl, Vojtěch Kloda, Daniel A. Keim +1

Advances in multimodal text-image models have enabled effective text-based querying in extensive image collections. While these models show convincing performance for everyday life…

cond-mat.dis-nn2025★ 5 cited

Hydrogen liquid-liquid transition from first principles and machine learning

Giacomo Tenti, Bastian Jäckl, Kousuke Nakano +2

The molecular-to-atomic liquid-liquid transition (LLT) in high-pressure hydrogen is a fundamental topic touching domains from planetary science to materials modeling. Yet, the natu…

cond-mat.mtrl-sci2024

Hydrogen under Pressure as a Benchmark for Machine-Learning Interatomic Potentials

Thomas Bischoff, Bastian Jäckl, Matthias Rupp

Machine-learning interatomic potentials (MLPs) are fast, data-driven surrogate models of atomistic systems' potential energy surfaces that can accelerate ab-initio molecular dynami…