5 papers
Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion
Prathami Divakar Kamath, Francesco Tavani, Alin Marin Elena +6
Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexi…
A chemical language model for reticular materials design
Dhruv Menon, Vivek Singh, Xu Chen +7
Reticular chemistry has enabled the synthesis of tens of thousands of metal-organic frameworks (MOFs), yet the discovery of new materials still relies largely on intuition-driven l…
System of Agentic AI for the Discovery of Metal-Organic Frameworks
Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan +12
Generative models and machine learning promise accelerated material discovery in MOFs for CO2 capture and water harvesting but face significant challenges navigating vast chemical…
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
Shengchao Liu, Weitao Du, Hannan Xu +8
In drug discovery, molecular dynamics (MD) simulation for protein-ligand binding provides a powerful tool for predicting binding affinities, estimating transport properties, and ex…
Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo
Nakul Rampal, Kaiyu Wang, Matthew Burigana +11
The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine…