Princeton researchers have developed a new tool to speed the discovery of advanced materials known as metal organic frameworks (MOFs).

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...

State-of-the-art machine learning models are often tested on their ability to generalize materials deemed ’dissimilar’ to training data, but such definitions frequently rely on heuristics. Here, an an...

About the Workshop

Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resul...

🎓 Reflecting on "Introduction to Machine Learning for Materials Science"🎓 As the semester comes to a close, it’s a great time to look back on the second…

Large language models (LLMs) have gained widespread interest due to their ability to process human language and perform tasks on which they have not been explicitly trained. However, we possess only...

Foundation Model for Materials - FM4M. Contribute to IBM/materials development by creating an account on GitHub.