601 citations · 1.2k across the 11 of their papers we have counts for
13 papers
GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generation
Hendrik Kraß, Seyed Mohamad Moosavi, Mathias Niepert
Generative models for crystals enable the discovery of novel structures, but scaling all-atom generation to larger systems such as metal--organic frameworks remains challenging. We…
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Aritra Roy, Kevin Shen, Andrew MacBride +350
Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broa…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
Chenru Duan, Aditya Nandy, Shyam Chand Pal +17
Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laboriou…
MOFSimBench: Evaluating Universal Machine Learning Interatomic Potentials In Metal--Organic Framework Molecular Modeling
Hendrik Kraß, Ju Huang, Seyed Mohamad Moosavi
Universal machine learning interatomic potentials (uMLIPs) have emerged as powerful tools for accelerating atomistic simulations, offering scalable and efficient modeling with accu…
CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction
Ella Miray Rajaonson, Mahyar Rajabi Kochi, Luis Martin Mejia Mendoza +2
Developing improved predictive models for multi-molecular systems is crucial, as nearly every chemical product used results from a mixture of chemicals. While being a vital part of…
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery
Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali +32
Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientifi…
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
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 hyb…
Agent-based Learning of Materials Datasets from Scientific Literature
Mehrad Ansari, Seyed Mohamad Moosavi
Advancements in machine learning and artificial intelligence are transforming materials discovery. Yet, the availability of structured experimental data remains a bottleneck. The v…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
Kevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi +1
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand…
Anomalous effects of velocity rescaling algorithms: the flying ice cube effect revisited
Efrem Braun, S. Mohamad Moosavi, Berend Smit
The flying ice cube effect is a molecular dynamics simulation artifact in which the use of velocity rescaling thermostats sometimes causes the violation of the equipartition theore…
Pore-geometry recognition: on the importance of quantifying similarity in nanoporous materials
Yongjin Lee, Senja D. Barthel, Paweł Dłotko +3
In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance…