Discovery of hydrogen storage molecules using large language models and machine learning

2026/01/01 by Hassan Harb, Magali Ferrandon, Timothy A. Goetjen +4 · 1 voice
Materials Science · Computer Science · Chemistry · #Machine Learning in Materials Science #Computational Drug Discovery Methods #Crystallography and molecular interactions

paper · doi:10.1039/d6dd00102e

openalex publication_date 2026/01/01 · openalex created_date 2026/05/01 · openalex updated_date 2026/06/11

Abstract

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design.

Citations

• Density-functional thermochemistry. III. The role of exact exchange
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
• ChatGPT for good? On opportunities and challenges of large language models for education
• TAGMol: Target-Aware Gradient-guided Molecule Generation
• Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

Discussions

• ☀️Happy Friday! Looking for some weekend reading?👇 Harb et al. introduce a framework for liquid organic hydrogen carrier (LOHC) candidate discovery using LLM-guided molecular generation and ML-based [bsky, 2 points, 0 comments]

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