Explainable Synthesizability Prediction of Inorganic Crystal Structures using Large Language Models
We evaluate the ability of machine learning to predict whether a hypothetical crystal
structure can be synthesized and explain those predictions to scientists. Fine-tuned large …
structure can be synthesized and explain those predictions to scientists. Fine-tuned large …
Accelerating materials recipe acquisition via LLM-mediated reinforcement learning
AJ Lew - MRS Advances, 2025 - Springer
Artificial intelligence (AI) has empowered materials research, enabling rapid property
prediction and inverse design. While machine learning can yield materials with desired …
prediction and inverse design. While machine learning can yield materials with desired …
Exploring the Chemical Design Space of Metal-Organic Frameworks for Photocatalysis
In this work, we introduce a combined DFT and machine learning approach to obtain
insights into the chemical design of metal-organic framework (MOF) photocatalysts for …
insights into the chemical design of metal-organic framework (MOF) photocatalysts for …
Which modern AI methods provide accurate predictions of toxicological endpoints? Analysis of Tox24 challenge results.
SA Eytcheson, IV Tetko - 2025 - chemrxiv.org
The Tox24 challenge was designed to evaluate the progress that has been made in
computational method development for the prediction of in vitro activity since the Tox21 …
computational method development for the prediction of in vitro activity since the Tox21 …