Marcel Baer | Searcharxiv

arXiv2026

Cryogenic characterisation for the Nulling Interferometry Cryogenic Experiment (NICE)

The Nulling Interferometry Cryogenic Experiment (NICE) is an experimental testbed for the beam combiner of the Large Interferometer For Exoplanets (LIFE) space mission. Until now, progress on NICE has been confined to an ambient bench, where we have recorded progress in deep ($<10^{-5}$) nulls at wavelengths between 4 and 5 microns at 300 K. However, the ultimate goal and requirement of NICE is to repeat these measurements at the sensitivity levels expected for a planetary system, requiring deep cryogenic conditions at 15 K. Here, we describe the ``Ice Cube'' cryostat, a small version of the future NICE cryostat that is used for component and subsystem level cryogenic testing. This is interfaced with a measurement setup using a segmented aperture interferometer and a wavefront sensor. We will also describe the testing campaign for understanding the material and mounting challenges that will be faced when translating the warm bench to cryogenic operations.

astro-ph.IM↗

arXiv2025

Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design

We present Genie-CAT, a tool-augmented large-language-model (LLM) system designed to accelerate scientific hypothesis generation in protein design. Using metalloproteins (e.g., ferredoxins) as a case study, Genie-CAT integrates four capabilities -- literature-grounded reasoning through retrieval-augmented generation (RAG), structural parsing of Protein Data Bank files, electrostatic potential calculations, and machine-learning prediction of redox properties -- into a unified agentic workflow. By coupling natural-language reasoning with data-driven and physics-based computation, the system generates mechanistically interpretable, testable hypotheses linking sequence, structure, and function. In proof-of-concept demonstrations, Genie-CAT autonomously identifies residue-level modifications near [Fe--S] clusters that affect redox tuning, reproducing expert-derived hypotheses in a fraction of the time. The framework highlights how AI agents combining language models with domain-specific tools can bridge symbolic reasoning and numerical simulation, transforming LLMs from conversational assistants into partners for computational discovery.

q-bio.QM↗