Publications by Sauradeep Majumdar

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The design and discovery of potential novel materials is critical for the advancement of climate change mitigation technologies. In this respect, metal-organic frameworks (MOFs) have received considerable attention over the last two decades. The combinatio ...

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronic ...

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...

Homochiral metal-organic frameworks (MOFs) are exceptional media for heterogeneous enantiodifferentiation processes. Modifying available achiral structure-bearing MOF scaffolds is a preferred method to extend this class of materials. Reported postsynthetic ...

Sauradeep Majumdar

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A diversity-driven discovery of potential nanoporous materials for energy-related applications

Assessment of fine-tuned large language models for real-world chemistry and material science applications

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

Nonuniform Chiralization of Metal-Organic Frameworks Using Imine Chemistry