10 papers
Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
Karn Tiwari, Niladri Dutta, N M Anoop Krishnan +1
Modeling interacting dynamical systems requires capturing spatial interactions alongside long-range temporal dependencies. Graph neural networks (GNNs) provide a natural representa…
GFFMERGE: Efficient Merging of Graph Neural Force Fields and Beyond
Parth Verma, Parv P. Singh, Vipul Garg +3
Graph Neural Networks (GNNs) have revolutionized Neural Force Fields for atomistic simulations, achieving near-quantum accuracy at reduced cost, yet adapting these models to new ch…
Beyond Manual Curation: Augmenting Targeted Protein Degradation Databases via Agentic Literature Extraction Workflows
Yaochen Rao, Farzaneh Jalalypour, N. M. Anoop Krishnan +1
Predictive models in biomedicine depend on structured assay data locked in the text, tables, and supplements of primary publications. This bottleneck is especially acute in targete…
Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery
Harshit Bisht, Vinay Kumar, Kevin Maik Jablonka +2
A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists,…
MDGYM: Benchmarking AI Agents on Molecular Simulations
Vinay Kumar, Satyendra Rajput, Mausam +1
The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin modern science. Molecular dy…
Sustainable Materials Discovery in the Era of Artificial Intelligence
Sajid Mannan, Rupert J. Myers, Rohit Batra +3
Artificial intelligence (AI) has transformed materials discovery, enabling rapid exploration of chemical space through generative models and surrogate screening. Yet current genera…