Vertaix (AI&Science Lab at Princeton University)
@vertaix.bsky.social
Research lab at Princeton University led by @adjiboussodieng.bsky.social. We work at the intersection of artificial intelligence (AI) and the natural sciences. Visit https://vertaix.princeton.edu to learn more. #AI4Science
Key ingredients to the solution we developed (continued): a novel sequence representation for MOFs (called MOFSeq), and a pretraining-finetuning strategy for LLM-Prop. 4/4
Check it out! #MatSci #MOFs #LLMs #ML
@chemrxiv.bsky.social link: go.shr.lc/4kIQoWN
Check it out! #MatSci #MOFs #LLMs #ML
@chemrxiv.bsky.social link: go.shr.lc/4kIQoWN
Highly Accurate and Fast Prediction of MOF Free Energy Via Machine Learning
A major bottleneck in translating computational screening of metal-organic frameworks (MOFs) to laboratory synthesis is the uncertain synthetic accessibility of computer-generated MOF designs. While M...
go.shr.lc
July 14, 2025 at 4:25 PM
Key ingredients to the solution we developed (continued): a novel sequence representation for MOFs (called MOFSeq), and a pretraining-finetuning strategy for LLM-Prop. 4/4
Check it out! #MatSci #MOFs #LLMs #ML
@chemrxiv.bsky.social link: go.shr.lc/4kIQoWN
Check it out! #MatSci #MOFs #LLMs #ML
@chemrxiv.bsky.social link: go.shr.lc/4kIQoWN
Our trained model is also able to accurately select the stable MOF polymorph in a polymorphic family, even when the free energy difference is as low as 0.16 kJ/mol.
Key ingredients to the solution we developed: a new dataset composed of 1 million MOF structures & their properties (MOFMinE) 3/4
Key ingredients to the solution we developed: a new dataset composed of 1 million MOF structures & their properties (MOFMinE) 3/4
July 14, 2025 at 4:25 PM
Our trained model is also able to accurately select the stable MOF polymorph in a polymorphic family, even when the free energy difference is as low as 0.16 kJ/mol.
Key ingredients to the solution we developed: a new dataset composed of 1 million MOF structures & their properties (MOFMinE) 3/4
Key ingredients to the solution we developed: a new dataset composed of 1 million MOF structures & their properties (MOFMinE) 3/4
The main bottleneck to accelerating the discovery of MOFs is synthesizability. In this paper, we propose a solution to this challenge by developing a holistic machine learning approach to predict MOF free energy and use that to predict MOF synthesizability with 97% accuracy! 2/4
July 14, 2025 at 4:25 PM
The main bottleneck to accelerating the discovery of MOFs is synthesizability. In this paper, we propose a solution to this challenge by developing a holistic machine learning approach to predict MOF free energy and use that to predict MOF synthesizability with 97% accuracy! 2/4
Aminah also published her first research paper in CHI 2025. The paper explored how #AI and #AR can be used to facilitate immersive Spanish group conversation practice. ✨ She's joining Apple as a Software Engineer in San Diego, CA. 2/2 #Vertaix #Princeton25
May 27, 2025 at 4:54 PM
Aminah also published her first research paper in CHI 2025. The paper explored how #AI and #AR can be used to facilitate immersive Spanish group conversation practice. ✨ She's joining Apple as a Software Engineer in San Diego, CA. 2/2 #Vertaix #Princeton25